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Planchuelo-Gómez Á, Descoteaux M, Larochelle H, Hutter J, Jones DK, Tax CMW. Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning. Med Image Anal 2024; 94:103134. [PMID: 38471339 DOI: 10.1016/j.media.2024.103134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 02/26/2024] [Accepted: 03/04/2024] [Indexed: 03/14/2024]
Abstract
Diffusion-relaxation MRI aims to extract quantitative measures that characterise microstructural tissue properties such as orientation, size, and shape, but long acquisition times are typically required. This work proposes a physics-informed learning framework to extract an optimal subset of diffusion-relaxation MRI measurements for enabling shorter acquisition times, predict non-measured signals, and estimate quantitative parameters. In vivo and synthetic brain 5D-Diffusion-T1-T2∗-weighted MRI data obtained from five healthy subjects were used for training and validation, and from a sixth participant for testing. One fully data-driven and two physics-informed machine learning methods were implemented and compared to two manual selection procedures and Cramér-Rao lower bound optimisation. The physics-informed approaches could identify measurement-subsets that yielded more consistently accurate parameter estimates in simulations than other approaches, with similar signal prediction error. Five-fold shorter protocols yielded error distributions of estimated quantitative parameters with very small effect sizes compared to estimates from the full protocol. Selected subsets commonly included a denser sampling of the shortest and longest inversion time, lowest echo time, and high b-value. The proposed framework combining machine learning and MRI physics offers a promising approach to develop shorter imaging protocols without compromising the quality of parameter estimates and signal predictions.
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Affiliation(s)
- Álvaro Planchuelo-Gómez
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom; Imaging Processing Laboratory, Universidad de Valladolid, Valladolid, Spain
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | | | - Jana Hutter
- Centre for Medical Engineering, Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Chantal M W Tax
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom.
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2
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Neves Silva S, McElroy S, Aviles Verdera J, Colford K, St Clair K, Tomi-Tricot R, Uus A, Ozenne V, Hall M, Story L, Pushparajah K, Rutherford MA, Hajnal JV, Hutter J. Fully automated planning for anatomical fetal brain MRI on 0.55T. Magn Reson Med 2024. [PMID: 38650351 DOI: 10.1002/mrm.30122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/08/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024]
Abstract
PURPOSE Widening the availability of fetal MRI with fully automatic real-time planning of radiological brain planes on 0.55T MRI. METHODS Deep learning-based detection of key brain landmarks on a whole-uterus echo planar imaging scan enables the subsequent fully automatic planning of the radiological single-shot Turbo Spin Echo acquisitions. The landmark detection pipeline was trained on over 120 datasets from varying field strength, echo times, and resolutions and quantitatively evaluated. The entire automatic planning solution was tested prospectively in nine fetal subjects between 20 and 37 weeks. A comprehensive evaluation of all steps, the distance between manual and automatic landmarks, the planning quality, and the resulting image quality was conducted. RESULTS Prospective automatic planning was performed in real-time without latency in all subjects. The landmark detection accuracy was 4.2± $$ \pm $$ 2.6 mm for the fetal eyes and 6.5± $$ \pm $$ 3.2 for the cerebellum, planning quality was 2.4/3 (compared to 2.6/3 for manual planning) and diagnostic image quality was 2.2 compared to 2.1 for manual planning. CONCLUSIONS Real-time automatic planning of all three key fetal brain planes was successfully achieved and will pave the way toward simplifying the acquisition of fetal MRI thereby widening the availability of this modality in nonspecialist centers.
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Affiliation(s)
- Sara Neves Silva
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Sarah McElroy
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK
| | - Jordina Aviles Verdera
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Kathleen Colford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Kamilah St Clair
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Raphael Tomi-Tricot
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK
| | - Alena Uus
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Valéry Ozenne
- CNRS, CRMSB, UMR 5536, IHU Liryc, Université de Bordeaux, Bordeaux, France
| | - Megan Hall
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Women & Children's Health, King's College London, London, UK
| | - Lisa Story
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Women & Children's Health, King's College London, London, UK
| | - Kuberan Pushparajah
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Smart Imaging Lab, Radiological Institute, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
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Hall M, de Marvao A, Schweitzer R, Cromb D, Colford K, Jandu P, O’Regan DP, Ho A, Price A, Chappell LC, Rutherford MA, Story L, Lamata P, Hutter J. Preeclampsia Associated Differences in the Placenta, Fetal Brain, and Maternal Heart Can Be Demonstrated Antenatally: An Observational Cohort Study Using MRI. Hypertension 2024; 81:836-847. [PMID: 38314606 PMCID: PMC7615760 DOI: 10.1161/hypertensionaha.123.22442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 01/02/2024] [Indexed: 02/06/2024]
Abstract
BACKGROUND Preeclampsia is a multiorgan disease of pregnancy that has short- and long-term implications for the woman and fetus, whose immediate impact is poorly understood. We present a novel multiorgan approach to magnetic resonance imaging (MRI) investigation of preeclampsia, with the acquisition of maternal cardiac, placental, and fetal brain anatomic and functional imaging. METHODS An observational study was performed recruiting 3 groups of pregnant women: those with preeclampsia, chronic hypertension, or no medical complications. All women underwent a cardiac MRI, and pregnant women underwent a placental-fetal MRI. Cardiac analysis for structural, morphological, and flow data were undertaken; placenta and fetal brain volumetric and T2* (which describes relative tissue oxygenation) data were obtained. All results were corrected for gestational age. A nonpregnant cohort was identified for inclusion in the statistical shape analysis. RESULTS Seventy-eight MRIs were obtained during pregnancy. Cardiac MRI analysis demonstrated higher left ventricular mass in preeclampsia with 3-dimensional modeling revealing additional specific characteristics of eccentricity and outflow track remodeling. Pregnancies affected by preeclampsia demonstrated lower placental and fetal brain T2*. Within the preeclampsia group, 23% placental T2* results were consistent with controls, these were the only cases with normal placental histopathology. Fetal brain T2* results were consistent with normal controls in 31% of cases. CONCLUSIONS We present the first holistic assessment of the immediate implications of preeclampsia on maternal heart, placenta, and fetal brain. As well as having potential clinical implications for the risk stratification and management of women with preeclampsia, this gives an insight into the disease mechanism.
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Affiliation(s)
- Megan Hall
- Department of Women and Children’s Health (M.H., A.d.M., A.H., L.C.C., L.S.), King’s College London, United Kingdom
- Centre for the Developing Brain (M.H., D.C., K.C., A.H., A.P., M.A.R., L.S., J.H.), King’s College London, United Kingdom
| | - Antonio de Marvao
- Department of Women and Children’s Health (M.H., A.d.M., A.H., L.C.C., L.S.), King’s College London, United Kingdom
- School of Cardiovascular Medicine (A.d.M., R.S.), King’s College London, United Kingdom
- MRC London Institute of Medical Sciences, Imperial College London, United Kingdom (A.d.M., R.S., D.P.O.)
| | - Ronny Schweitzer
- School of Cardiovascular Medicine (A.d.M., R.S.), King’s College London, United Kingdom
- MRC London Institute of Medical Sciences, Imperial College London, United Kingdom (A.d.M., R.S., D.P.O.)
| | - Daniel Cromb
- Centre for the Developing Brain (M.H., D.C., K.C., A.H., A.P., M.A.R., L.S., J.H.), King’s College London, United Kingdom
| | - Kathleen Colford
- Centre for the Developing Brain (M.H., D.C., K.C., A.H., A.P., M.A.R., L.S., J.H.), King’s College London, United Kingdom
| | - Priya Jandu
- GKT School of Medical Education (P.J.), King’s College London, United Kingdom
| | - Declan P O’Regan
- MRC London Institute of Medical Sciences, Imperial College London, United Kingdom (A.d.M., R.S., D.P.O.)
| | - Alison Ho
- Department of Women and Children’s Health (M.H., A.d.M., A.H., L.C.C., L.S.), King’s College London, United Kingdom
- Centre for the Developing Brain (M.H., D.C., K.C., A.H., A.P., M.A.R., L.S., J.H.), King’s College London, United Kingdom
| | - Anthony Price
- Centre for the Developing Brain (M.H., D.C., K.C., A.H., A.P., M.A.R., L.S., J.H.), King’s College London, United Kingdom
- Centre for Medical Engineering (A.P., P.L.), King’s College London, United Kingdom
| | - Lucy C. Chappell
- Department of Women and Children’s Health (M.H., A.d.M., A.H., L.C.C., L.S.), King’s College London, United Kingdom
| | - Mary A. Rutherford
- Centre for the Developing Brain (M.H., D.C., K.C., A.H., A.P., M.A.R., L.S., J.H.), King’s College London, United Kingdom
| | - Lisa Story
- Department of Women and Children’s Health (M.H., A.d.M., A.H., L.C.C., L.S.), King’s College London, United Kingdom
- Centre for the Developing Brain (M.H., D.C., K.C., A.H., A.P., M.A.R., L.S., J.H.), King’s College London, United Kingdom
| | - Pablo Lamata
- Centre for Medical Engineering (A.P., P.L.), King’s College London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain (M.H., D.C., K.C., A.H., A.P., M.A.R., L.S., J.H.), King’s College London, United Kingdom
- Smart Imaging Lab, Radiological Institute, University Hospital Erlangen, Germany (J.H.)
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Uus AU, Hall M, Grigorescu I, Avena Zampieri C, Egloff Collado A, Payette K, Matthew J, Kyriakopoulou V, Hajnal JV, Hutter J, Rutherford MA, Deprez M, Story L. Automated body organ segmentation, volumetry and population-averaged atlas for 3D motion-corrected T2-weighted fetal body MRI. Sci Rep 2024; 14:6637. [PMID: 38503833 PMCID: PMC10950851 DOI: 10.1038/s41598-024-57087-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 03/14/2024] [Indexed: 03/21/2024] Open
Abstract
Structural fetal body MRI provides true 3D information required for volumetry of fetal organs. However, current clinical and research practice primarily relies on manual slice-wise segmentation of raw T2-weighted stacks, which is time consuming, subject to inter- and intra-observer bias and affected by motion-corruption. Furthermore, there are no existing standard guidelines defining a universal approach to parcellation of fetal organs. This work produces the first parcellation protocol of the fetal body organs for motion-corrected 3D fetal body MRI. It includes 10 organ ROIs relevant to fetal quantitative volumetry studies. We also introduce the first population-averaged T2w MRI atlas of the fetal body. The protocol was used as a basis for training of a neural network for automated organ segmentation. It showed robust performance for different gestational ages. This solution minimises the need for manual editing and significantly reduces time. The general feasibility of the proposed pipeline was also assessed by analysis of organ growth charts created from automated parcellations of 91 normal control 3T MRI datasets that showed expected increase in volumetry during 22-38 weeks gestational age range.
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Affiliation(s)
- Alena U Uus
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
| | - Megan Hall
- Centre for the Developing Brain, King's College London, London, UK
- Department of Women and Children's Health, King's College London, London, UK
- Fetal Medicine Unit, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Irina Grigorescu
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Carla Avena Zampieri
- Centre for the Developing Brain, King's College London, London, UK
- Department of Women and Children's Health, King's College London, London, UK
| | | | - Kelly Payette
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Jacqueline Matthew
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | | | - Joseph V Hajnal
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Jana Hutter
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
- Smart Imaging Lab, Radiological Institute, University Hospital Erlangen, Erlangen, Germany
| | | | - Maria Deprez
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Lisa Story
- Centre for the Developing Brain, King's College London, London, UK
- Department of Women and Children's Health, King's College London, London, UK
- Fetal Medicine Unit, Guy's and St Thomas' NHS Foundation Trust, London, UK
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5
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Matthew J, Uus A, De Souza L, Wright R, Fukami-Gartner A, Priego G, Saija C, Deprez M, Collado AE, Hutter J, Story L, Malamateniou C, Rhode K, Hajnal J, Rutherford MA. Craniofacial phenotyping with fetal MRI: a feasibility study of 3D visualisation, segmentation, surface-rendered and physical models. BMC Med Imaging 2024; 24:52. [PMID: 38429666 PMCID: PMC10905839 DOI: 10.1186/s12880-024-01230-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 02/19/2024] [Indexed: 03/03/2024] Open
Abstract
This study explores the potential of 3D Slice-to-Volume Registration (SVR) motion-corrected fetal MRI for craniofacial assessment, traditionally used only for fetal brain analysis. In addition, we present the first description of an automated pipeline based on 3D Attention UNet trained for 3D fetal MRI craniofacial segmentation, followed by surface refinement. Results of 3D printing of selected models are also presented.Qualitative analysis of multiplanar volumes, based on the SVR output and surface segmentations outputs, were assessed with computer and printed models, using standardised protocols that we developed for evaluating image quality and visibility of diagnostic craniofacial features. A test set of 25, postnatally confirmed, Trisomy 21 fetal cases (24-36 weeks gestational age), revealed that 3D reconstructed T2 SVR images provided 66-100% visibility of relevant craniofacial and head structures in the SVR output, and 20-100% and 60-90% anatomical visibility was seen for the baseline and refined 3D computer surface model outputs respectively. Furthermore, 12 of 25 cases, 48%, of refined surface models demonstrated good or excellent overall quality with a further 9 cases, 36%, demonstrating moderate quality to include facial, scalp and external ears. Additional 3D printing of 12 physical real-size models (20-36 weeks gestational age) revealed good/excellent overall quality in all cases and distinguishable features between healthy control cases and cases with confirmed anomalies, with only minor manual adjustments required before 3D printing.Despite varying image quality and data heterogeneity, 3D T2w SVR reconstructions and models provided sufficient resolution for the subjective characterisation of subtle craniofacial features. We also contributed a publicly accessible online 3D T2w MRI atlas of the fetal head, validated for accurate representation of normal fetal anatomy.Future research will focus on quantitative analysis, optimizing the pipeline, and exploring diagnostic, counselling, and educational applications in fetal craniofacial assessment.
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Affiliation(s)
- Jacqueline Matthew
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.
- Guy's and St Thomas' NHS Foundation Trust, London, UK.
| | - Alena Uus
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Leah De Souza
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Robert Wright
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Abi Fukami-Gartner
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Gema Priego
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
- Barking, Havering and Redbridge University Hospitals NHS Trust, London, UK
| | - Carlo Saija
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Maria Deprez
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Alexia Egloff Collado
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Jana Hutter
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Lisa Story
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | | | - Kawal Rhode
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Jo Hajnal
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Mary A Rutherford
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
- Guy's and St Thomas' NHS Foundation Trust, London, UK
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Hall M, Hutter J, Uus A, du Crest E, Egloff A, Suff N, Al Adnani M, Seed PT, Gibbons D, Deprez M, Tribe RM, Shennan A, Rutherford M, Story L. Adrenal volumes in fetuses delivering prior to 32 weeks' gestation: An MRI pilot study. Acta Obstet Gynecol Scand 2024; 103:512-521. [PMID: 38009386 PMCID: PMC10867361 DOI: 10.1111/aogs.14733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 11/28/2023]
Abstract
INTRODUCTION Spontaneous preterm birth prior to 32 weeks' gestation accounts for 1% of all deliveries and is associated with high rates of morbidity and mortality. A total of 70% are associated with chorioamnionitis which increases the incidence of morbidity, but for which there is no noninvasive antenatal test. Fetal adrenal glands produce cortisol and dehydroepiandosterone-sulphate which upregulate prior to spontaneous preterm birth. Ultrasound suggests that adrenal volumes may increase prior to preterm birth, but studies are limited. This study aimed to: (i) demonstrate reproducibility of magnetic resonance imaging (MRI) derived adrenal volumetry; (ii) derive normal ranges of total adrenal volumes, and adrenal: body volume for normal; (iii) compare with those who have spontaneous very preterm birth; and (iv) correlate with histopathological chorioamnionitis. MATERIAL AND METHODS Patients at high risk of preterm birth prior to 32 weeks were prospectively recruited, and included if they did deliver prior to 32 weeks; a control group who delivered an uncomplicated pregnancy at term was also recruited. T2 weighted images of the entire uterus were obtained, and a deformable slice-to-volume method was used to reconstruct the fetal abdomen. Adrenal and body volumes were obtained via manual segmentation, and adrenal: body volume ratios generated. Normal ranges were created using control data. Differences between groups were investigated accounting for the effect of gestation by use of regression analysis. Placental histopathology was reviewed for pregnancies delivering preterm. RESULTS A total of 56 controls and 26 cases were included in the analysis. Volumetry was consistent between observers. Adrenal volumes were not higher in the case group (p = 0.2); adrenal: body volume ratios were higher (p = 0.011), persisting in the presence of chorioamnionitis (p = 0.017). A cluster of three pairs of adrenal glands below the fifth centile were noted among the cases all of whom had a protracted period at risk of preterm birth prior to MRI. CONCLUSIONS Adrenal: body volume ratios are significantly larger in fetuses who go on to deliver preterm than those delivering at term. Adrenal volumes were not significantly larger, we hypothesize that this could be due to an adrenal atrophy in fetuses with fulminating chorioamnionitis. A straightforward relationship of adrenal size being increased prior to preterm birth should not be assumed.
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Affiliation(s)
- Megan Hall
- Center for the Developing BrainSt Thomas' Hospital, King's College LondonLondonUK
- Department of Women and Children's HealthSt Thomas' Hospital, King's College LondonLondonUK
| | - Jana Hutter
- Center for the Developing BrainSt Thomas' Hospital, King's College LondonLondonUK
| | - Alena Uus
- Center for the Developing BrainSt Thomas' Hospital, King's College LondonLondonUK
| | - Elise du Crest
- Department of Women and Children's HealthSt Thomas' Hospital, King's College LondonLondonUK
| | - Alexia Egloff
- Center for the Developing BrainSt Thomas' Hospital, King's College LondonLondonUK
| | - Natalie Suff
- Department of Women and Children's HealthSt Thomas' Hospital, King's College LondonLondonUK
| | - Mudher Al Adnani
- Department of Cellular PathologySt Thomas' Hospital, Guy's and St Thomas' NHS Foundation TrustLondonUK
| | - Paul T. Seed
- Department of Women and Children's HealthSt Thomas' Hospital, King's College LondonLondonUK
| | - Deena Gibbons
- Department of ImmunobiologyKing's College LondonLondonUK
| | - Maria Deprez
- Center for the Developing BrainSt Thomas' Hospital, King's College LondonLondonUK
| | - Rachel M. Tribe
- Department of Women and Children's HealthSt Thomas' Hospital, King's College LondonLondonUK
| | - Andrew Shennan
- Department of Women and Children's HealthSt Thomas' Hospital, King's College LondonLondonUK
| | - Mary Rutherford
- Center for the Developing BrainSt Thomas' Hospital, King's College LondonLondonUK
| | - Lisa Story
- Center for the Developing BrainSt Thomas' Hospital, King's College LondonLondonUK
- Department of Women and Children's HealthSt Thomas' Hospital, King's College LondonLondonUK
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7
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Ball G, Oldham S, Kyriakopoulou V, Williams LZJ, Karolis V, Price A, Hutter J, Seal ML, Alexander-Bloch A, Hajnal JV, Edwards AD, Robinson EC, Seidlitz J. Molecular signatures of cortical expansion in the human fetal brain. bioRxiv 2024:2024.02.13.580198. [PMID: 38405710 PMCID: PMC10888819 DOI: 10.1101/2024.02.13.580198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
The third trimester of human gestation is characterised by rapid increases in brain volume and cortical surface area. A growing catalogue of cells in the prenatal brain has revealed remarkable molecular diversity across cortical areas.1,2 Despite this, little is known about how this translates into the patterns of differential cortical expansion observed in humans during the latter stages of gestation. Here we present a new resource, μBrain, to facilitate knowledge translation between molecular and anatomical descriptions of the prenatal developing brain. Built using generative artificial intelligence, μBrain is a three-dimensional cellular-resolution digital atlas combining publicly-available serial sections of the postmortem human brain at 21 weeks gestation3 with bulk tissue microarray data, sampled across 29 cortical regions and 5 transient tissue zones.4 Using μBrain, we evaluate the molecular signatures of preferentially-expanded cortical regions during human gestation, quantified in utero using magnetic resonance imaging (MRI). We find that differences in the rates of expansion across cortical areas during gestation respect anatomical and evolutionary boundaries between cortical types5 and are founded upon extended periods of upper-layer cortical neuron migration that continue beyond mid-gestation. We identify a set of genes that are upregulated from mid-gestation and highly expressed in rapidly expanding neocortex, which are implicated in genetic disorders with cognitive sequelae. Our findings demonstrate a spatial coupling between areal differences in the timing of neurogenesis and rates of expansion across the neocortical sheet during the prenatal epoch. The μBrain atlas is available from: https://garedaba.github.io/micro-brain/ and provides a new tool to comprehensively map early brain development across domains, model systems and resolution scales.
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Affiliation(s)
- G Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
| | - S Oldham
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
| | - V Kyriakopoulou
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - L Z J Williams
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - V Karolis
- Centre for the Developing Brain, King's College London, London, UK
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - A Price
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - J Hutter
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - M L Seal
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
| | - A Alexander-Bloch
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, The Children's Hospital of Philadelphia, Philadelphia, PA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA
| | - J V Hajnal
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - A D Edwards
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - E C Robinson
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - J Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, The Children's Hospital of Philadelphia, Philadelphia, PA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA
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8
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Avena-Zampieri CL, Hutter J, Uus A, Deprez M, Payette K, Hall M, Bafadhel M, Russell REK, Milan A, Rutherford M, Shennan A, Greenough A, Story L. Functional MRI assessment of the lungs in fetuses that deliver very Preterm: An MRI pilot study. Eur J Obstet Gynecol Reprod Biol 2024; 293:106-114. [PMID: 38141484 PMCID: PMC10929943 DOI: 10.1016/j.ejogrb.2023.12.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 12/11/2023] [Indexed: 12/25/2023]
Abstract
OBJECTIVES To compare mean pulmonary T2* values and pulmonary volumes in fetuses that subsequently spontaneously delivered before 32 weeks with a control cohort with comparable gestational ages and to assess the value of mean pulmonary T2* as a predictor of preterm birth < 32 weeks' gestation. METHODS MRI datasets scanned at similar gestational ages were selected from fetuses who spontaneously delivered < 32 weeks of gestation and a control group who subsequently delivered at term with no complications. All women underwent a fetal MRI on a 3 T MRI imaging system. Sequences included T2-weighted single shot fast spin echo and T2* sequences, using gradient echo single shot echo planar sequencing of the fetal thorax. Motion correction was performed using slice-to-volume reconstruction and T2* maps generated using in-house pipelines. Lungs were manually segmented and volumes and mean T2* values calculated for both lungs combined and left and right lung separately. Linear regression was used to compare values between the preterm and control cohorts accounting for the effects of gestation. Receiver operating curves were generated for mean T2* values and pulmonary volume as predictors of preterm birth < 32 weeks' gestation. RESULTS Datasets from twenty-eight preterm and 74 control fetuses were suitable for analysis. MRI images were taken at similar fetal gestational ages (preterm cohort (mean ± SD) 24.9 ± 3.3 and control cohort (mean ± SD) 26.5 ± 3.0). Mean gestational age at delivery was 26.4 ± 3.3 for the preterm group and 39.9 ± 1.3 for the control group. Mean pulmonary T2* values remained constant with increasing gestational age while pulmonary volumes increased. Both T2* and pulmonary volumes were lower in the preterm group than in the control group for all parameters (both combined, left, and right lung (p < 0.001 in all cases). Adjusted for gestational age, pulmonary volumes and mean T2* values were good predictors of premature delivery in fetuses < 32 weeks (area under the curve of 0.828 and 0.754 respectively). CONCLUSION These findings indicate that mean pulmonary T2* values and volumes were lower in fetuses that subsequently delivered very preterm. This may suggest potentially altered oxygenation and indicate that pulmonary morbidity associated with prematurity has an antenatal antecedent. Future work should explore these results correlating antenatal findings with long term pulmonary outcomes.
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Affiliation(s)
- Carla L Avena-Zampieri
- Department of Women and Children's Health King's College London, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom
| | - Alena Uus
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom; Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom
| | - Maria Deprez
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom; Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom
| | - Kelly Payette
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom; Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom
| | - Megan Hall
- Department of Women and Children's Health King's College London, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom; Fetal Medicine Unit, Guy's and St Thomas' NHS Foundation Trust, United Kingdom
| | - Mona Bafadhel
- King's Centre for Lung Health, School of Immunology and Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Richard E K Russell
- King's Centre for Lung Health, School of Immunology and Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Anna Milan
- Neonatal Unit, Guy's and St Thomas' NHS Foundation Trust, United Kingdom
| | - Mary Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom
| | - Andrew Shennan
- Department of Women and Children's Health King's College London, United Kingdom
| | - Anne Greenough
- Department of Women and Children's Health King's College London, United Kingdom
| | - Lisa Story
- Department of Women and Children's Health King's College London, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom; Fetal Medicine Unit, Guy's and St Thomas' NHS Foundation Trust, United Kingdom
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9
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Cromb D, Slator P, Hall M, Price A, Alexander D, Counsell S, Hutter J. Advanced magnetic resonance imaging detects altered placental development in pregnancies affected by congenital heart disease. Res Sq 2024:rs.3.rs-3873412. [PMID: 38343847 PMCID: PMC10854304 DOI: 10.21203/rs.3.rs-3873412/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
Congenital heart disease (CHD) is the most common congenital malformation and is associated with adverse neurodevelopmental outcomes. The placenta is crucial for healthy fetal development and placental development is altered in pregnancy when the fetus has CHD. This study utilized advanced combined diffusion-relaxation MRI and a data-driven analysis technique to test the hypothesis that placental microstructure and perfusion are altered in CHD-affected pregnancies. 48 participants (36 controls, 12 CHD) underwent 67 MRI scans (50 control, 17 CHD). Significant differences in the weighting of two independent placental and uterine-wall tissue components were identified between the CHD and control groups (both pFDR<0.001), with changes most evident after 30 weeks gestation. A Significant trend over gestation in weighting for a third independent tissue component was also observed in the CHD cohort (R = 0.50, pFDR=0.04), but not in controls. These findings add to existing evidence that placental development is altered in CHD. The results may reflect alterations in placental perfusion or the changes in fetal-placental flow, villous structure and maturation that occur in CHD. Further research is needed to validate and better understand these findings and to understand the relationship between placental development, CHD, and its neurodevelopmental implications.
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10
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Story L, Uus A, Hall M, Payette K, Bakalis S, Arichi T, Shennan A, Rutherford M, Hutter J. Functional assessment of brain development in fetuses that subsequently deliver very preterm: An MRI pilot study. Prenat Diagn 2024; 44:49-56. [PMID: 38126921 PMCID: PMC10952951 DOI: 10.1002/pd.6498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/14/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023]
Abstract
OBJECTIVES To evaluate changes occurring in the fetal brain prior to very preterm delivery using MRI T2* relaxometry, an indirect assessment of tissue perfusion. METHOD Fetuses that subsequently delivered spontaneously <32 weeks gestation and a control cohort were identified from pre-existing datasets. Participants had undergone a 3T MRI assessment including T2* relaxometry of the fetal brain using a 2D multi-slice gradient echo single shot echo planar imaging sequence. T2* maps were generated, supratentorial brain tissue was manually segmented and mean T2* values were generated. Groups were compared using quadratic regression. RESULTS Twenty five fetuses that subsequently delivered <32 weeks and 67 that delivered at term were included. Mean gestation at MRI was 24.5 weeks (SD 3.3) and 25.4 weeks (SD 3.1) and gestation at delivery 25.5 weeks (SD 3.4) and 39.7 weeks (SD 1.2) in the preterm and term cohorts respectively. Brain mean T2* values were significantly lower in fetuses that subsequently delivered before 32 weeks gestation (p < 0.001). CONCLUSION Alterations in brain maturation appear to occur prior to preterm delivery. Further work is required to explore these associations, but these findings suggest a potential window for therapeutic neuroprotective agents in fetuses at high risk of preterm delivery in the future.
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Affiliation(s)
- Lisa Story
- Department of Women's and Children's HealthKing's College LondonSt Thomas' Hospital LondonLondonUK
- Centre for the Developing BrainSchool of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' Hospital LondonLondonUK
- Fetal Medicine UnitSt Thomas' Hospital LondonLondonUK
| | - Alena Uus
- Department of Women's and Children's HealthKing's College LondonSt Thomas' Hospital LondonLondonUK
| | - Megan Hall
- Department of Women's and Children's HealthKing's College LondonSt Thomas' Hospital LondonLondonUK
- Centre for the Developing BrainSchool of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' Hospital LondonLondonUK
| | - Kelly Payette
- Department of Women's and Children's HealthKing's College LondonSt Thomas' Hospital LondonLondonUK
| | | | - Tomoki Arichi
- Centre for the Developing BrainSchool of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' Hospital LondonLondonUK
| | - Andrew Shennan
- Department of Women's and Children's HealthKing's College LondonSt Thomas' Hospital LondonLondonUK
| | - Mary Rutherford
- Centre for the Developing BrainSchool of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' Hospital LondonLondonUK
| | - Jana Hutter
- Centre for the Developing BrainSchool of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' Hospital LondonLondonUK
- Radiological InstituteUniversity Hospital ErlangenErlangenGermany
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11
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Neves Silva S, Aviles Verdera J, Tomi‐Tricot R, Neji R, Uus A, Grigorescu I, Wilkinson T, Ozenne V, Lewin A, Story L, De Vita E, Rutherford M, Pushparajah K, Hajnal J, Hutter J. Real-time fetal brain tracking for functional fetal MRI. Magn Reson Med 2023; 90:2306-2320. [PMID: 37465882 PMCID: PMC10952752 DOI: 10.1002/mrm.29803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 07/03/2023] [Accepted: 07/03/2023] [Indexed: 07/20/2023]
Abstract
PURPOSE To improve motion robustness of functional fetal MRI scans by developing an intrinsic real-time motion correction method. MRI provides an ideal tool to characterize fetal brain development and growth. It is, however, a relatively slow imaging technique and therefore extremely susceptible to subject motion, particularly in functional MRI experiments acquiring multiple Echo-Planar-Imaging-based repetitions, for example, diffusion MRI or blood-oxygen-level-dependency MRI. METHODS A 3D UNet was trained on 125 fetal datasets to track the fetal brain position in each repetition of the scan in real time. This tracking, inserted into a Gadgetron pipeline on a clinical scanner, allows updating the position of the field of view in a modified echo-planar imaging sequence. The method was evaluated in real-time in controlled-motion phantom experiments and ten fetal MR studies (17 + 4-34 + 3 gestational weeks) at 3T. The localization network was additionally tested retrospectively on 29 low-field (0.55T) datasets. RESULTS Our method achieved real-time fetal head tracking and prospective correction of the acquisition geometry. Localization performance achieved Dice scores of 84.4% and 82.3%, respectively for both the unseen 1.5T/3T and 0.55T fetal data, with values higher for cephalic fetuses and increasing with gestational age. CONCLUSIONS Our technique was able to follow the fetal brain even for fetuses under 18 weeks GA in real-time at 3T and was successfully applied "offline" to new cohorts on 0.55T. Next, it will be deployed to other modalities such as fetal diffusion MRI and to cohorts of pregnant participants diagnosed with pregnancy complications, for example, pre-eclampsia and congenital heart disease.
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Affiliation(s)
- Sara Neves Silva
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Jordina Aviles Verdera
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Raphael Tomi‐Tricot
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- MR Research CollaborationsSiemens Healthcare LimitedCamberleyUK
| | - Radhouene Neji
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- MR Research CollaborationsSiemens Healthcare LimitedCamberleyUK
| | - Alena Uus
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Irina Grigorescu
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Thomas Wilkinson
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Valery Ozenne
- CNRS, CRMSB, UMR 5536, IHU LirycUniversité de BordeauxBordeauxFrance
| | - Alexander Lewin
- Institute of Neuroscience and Medicine 11, INM‐11Forschungszentrum JülichJülichGermany
- RWTHAachen UniversityAachenGermany
| | - Lisa Story
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Department of Women & Children's HealthKing's College LondonLondonUK
| | - Enrico De Vita
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- MRI Physics GroupGreat Ormond Street HospitalLondonUK
| | - Mary Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Kuberan Pushparajah
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Jo Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
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12
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Slator PJ, Cromb D, Jackson LH, Ho A, Counsell SJ, Story L, Chappell LC, Rutherford M, Hajnal JV, Hutter J, Alexander DC. Non-invasive mapping of human placenta microenvironments throughout pregnancy with diffusion-relaxation MRI. Placenta 2023; 144:29-37. [PMID: 37952367 DOI: 10.1016/j.placenta.2023.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 10/13/2023] [Accepted: 11/01/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION In-vivo measurements of placental structure and function have the potential to improve prediction, diagnosis, and treatment planning for a wide range of pregnancy complications, such as fetal growth restriction and pre-eclampsia, and hence inform clinical decision making, ultimately improving patient outcomes. MRI is emerging as a technique with increased sensitivity to placental structure and function compared to the current clinical standard, ultrasound. METHODS We demonstrate and evaluate a combined diffusion-relaxation MRI acquisition and analysis pipeline on a sizable cohort of 78 normal pregnancies with gestational ages ranging from 15 + 5 to 38 + 4 weeks. Our acquisition comprises a combined T2*-diffusion MRI acquisition sequence - which is simultaneously sensitive to oxygenation, microstructure and microcirculation. We analyse our scans with a data-driven unsupervised machine learning technique, InSpect, that parsimoniously identifies distinct components in the data. RESULTS We identify and map seven potential placental microenvironments and reveal detailed insights into multiple microstructural and microcirculatory features of the placenta, and assess their trends across gestation. DISCUSSION By demonstrating direct observation of micro-scale placental structure and function, and revealing clear trends across pregnancy, our work contributes towards the development of robust imaging biomarkers for pregnancy complications and the ultimate goal of a normative model of placental development.
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Affiliation(s)
- Paddy J Slator
- Cardiff University Brain Research Imaging Centre, School of Psychology, Maindy Road, Cardiff, CF24 4HQ, UK; School of Computer Science and Informatics, Cardiff University, Cardiff, UK; Centre for Medical Image Computing and Department of Computer Science, University College London, London, UK.
| | - Daniel Cromb
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Laurence H Jackson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Alison Ho
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Lisa Story
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Lucy C Chappell
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Mary Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, UK
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13
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McIlvain G, Oechtering TH, Shammi UA, Bhayana R, Hutter J, Moy L, Schweitzer M. Chatbots for Literature Review and Research-Insights from a Panel Discussion at the Annual Meeting of the International Society of Magnetic Resonance in Medicine (ISMRM) 2023. J Magn Reson Imaging 2023. [PMID: 37795851 DOI: 10.1002/jmri.29036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/07/2023] [Accepted: 09/19/2023] [Indexed: 10/06/2023] Open
Affiliation(s)
- Grace McIlvain
- Department of Biomedical Engineering, Columbia University, New York City, New York, USA
| | - Thekla H Oechtering
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Radiology and Nuclear Medicine, University of Luebeck, Lübeck, Germany
| | - Ummul Afia Shammi
- Chemical and Biomedical Engineering, University of Missouri, Columbia, Missouri, USA
| | - Rajesh Bhayana
- Department of Medical Imaging, University Health Network Mount Sinai Hospital and Women's College Hospital University of Toronto, Toronto, Ontario, Canada
| | - Jana Hutter
- Centre for the Developing Brain, King's College London, UK
| | - Linda Moy
- Department of Radiology, New York University School of Medicine, New York City, New York, USA
| | - Mark Schweitzer
- Wayne State University School of Medicine, Detroit, Michigan, USA
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14
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Aviles Verdera J, Story L, Hall M, Finck T, Egloff A, Seed PT, Malik SJ, Rutherford MA, Hajnal JV, Tomi-Tricot R, Hutter J. Reliability and Feasibility of Low-Field-Strength Fetal MRI at 0.55 T during Pregnancy. Radiology 2023; 309:e223050. [PMID: 37847139 PMCID: PMC10623193 DOI: 10.1148/radiol.223050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 08/20/2023] [Accepted: 09/06/2023] [Indexed: 10/18/2023]
Abstract
Background The benefits of using low-field-strength fetal MRI to evaluate antenatal development include reduced image artifacts, increased comfort, larger bore size, and potentially reduced costs, but studies about fetal low-field-strength MRI are lacking. Purpose To evaluate the reliability and feasibility of low-field-strength fetal MRI to assess anatomic and functional measures in pregnant participants using a commercially available 0.55-T MRI scanner and a comprehensive 20-minute protocol. Materials and Methods This prospective study was performed at a large teaching hospital (St Thomas' Hospital; London, England) from May to November 2022 in healthy pregnant participants and participants with pregnancy-related abnormalities using a commercially available 0.55-T MRI scanner. A 20-minute protocol was acquired including anatomic T2-weighted fast-spin-echo, quantitative T2*, and diffusion sequences. Key measures like biparietal diameter, transcerebellar diameter, lung volume, and cervical length were evaluated by two radiologists and an MRI-experienced obstetrician. Functional organ-specific mean values were given. Comparison was performed with existing published values and higher-field MRI using linear regression, interobserver correlation, and Bland-Altman plots. Results A total of 79 fetal MRI examinations were performed (mean gestational age, 29.4 weeks ± 5.5 [SD] [age range, 17.6-39.3 weeks]; maternal age, 34.4 years ± 5.3 [age range, 18.4-45.5 years]) in 47 healthy pregnant participants (control participants) and in 32 participants with pregnancy-related abnormalities. The key anatomic two-dimensional measures for the 47 healthy participants agreed with large cross-sectional 1.5-T and 3-T control studies. The interobserver correlations for the biparietal diameter in the first 40 consecutive scans were 0.96 (95% CI: 0.7, 0.99; P = .002) for abnormalities and 0.93 (95% CI: 0.86, 0.97; P < .001) for control participants. Functional features, including placental and brain T2* and placental apparent diffusion coefficient values, strongly correlated with gestational age (mean placental T2* in the control participants: 5.2 msec of decay per week; R2 = 0.66; mean T2* at 30 weeks, 176.6 msec; P < .001). Conclusion The 20-minute low-field-strength fetal MRI examination protocol was capable of producing reliable structural and functional measures of the fetus and placenta in pregnancy. Clinical trial registration no. REC 21/LO/0742 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Gowland in this issue.
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Affiliation(s)
- Jordina Aviles Verdera
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| | - Lisa Story
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| | - Megan Hall
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| | - Tom Finck
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| | - Alexia Egloff
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| | - Paul T. Seed
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| | - Shaihan J. Malik
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| | - Mary A. Rutherford
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| | - Joseph V. Hajnal
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| | - Raphaël Tomi-Tricot
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| | - Jana Hutter
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
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15
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Uus AU, Hall M, Grigorescu I, Zampieri CA, Collado AE, Payette K, Matthew J, Kyriakopoulou V, Hajnal JV, Hutter J, Rutherford MA, Deprez M, Story L. 3D T2w fetal body MRI: automated organ volumetry, growth charts and population-averaged atlas. medRxiv 2023:2023.05.31.23290751. [PMID: 37398121 PMCID: PMC10312818 DOI: 10.1101/2023.05.31.23290751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Structural fetal body MRI provides true 3D information required for volumetry of fetal organs. However, current clinical and research practice primarily relies on manual slice-wise segmentation of raw T2-weighted stacks, which is time consuming, subject to inter- and intra-observer bias and affected by motion-corruption. Furthermore, there are no existing standard guidelines defining a universal approach to parcellation of fetal organs. This work produces the first parcellation protocol of the fetal body organs for motion-corrected 3D fetal body MRI. It includes 10 organ ROIs relevant to fetal quantitative volumetry studies. We also introduce the first population-averaged T2w MRI atlas of the fetal body. The protocol was used as a basis for training of a neural network for automated organ segmentation. It showed robust performance for different gestational ages. This solution minimises the need for manual editing and significantly reduces time. The general feasibility of the proposed pipeline was also assessed by analysis of organ growth charts created from automated parcellations of 91 normal control 3T MRI datasets that showed expected increase in volumetry during 22-38 weeks gestational age range. In addition, the results of comparison between 60 normal and 12 fetal growth restriction datasets revealed significant differences in organ volumes.
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Affiliation(s)
- Alena U. Uus
- School of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Megan Hall
- Centre for the Developing Brain, King’s College London, London, UK
- Department of Women and Children’s Health, King’s College London, London, UK
- Fetal Medicine Unit, Guy’s and St Thomas’ NHS Foundation Trust, London, UK
| | - Irina Grigorescu
- School of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Carla Avena Zampieri
- Centre for the Developing Brain, King’s College London, London, UK
- Department of Women and Children’s Health, King’s College London, London, UK
| | | | - Kelly Payette
- School of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
- Centre for the Developing Brain, King’s College London, London, UK
| | - Jacqueline Matthew
- School of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
- Centre for the Developing Brain, King’s College London, London, UK
| | | | - Joseph V. Hajnal
- School of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
- Centre for the Developing Brain, King’s College London, London, UK
| | - Jana Hutter
- School of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
- Centre for the Developing Brain, King’s College London, London, UK
| | | | - Maria Deprez
- School of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Lisa Story
- Centre for the Developing Brain, King’s College London, London, UK
- Department of Women and Children’s Health, King’s College London, London, UK
- Fetal Medicine Unit, Guy’s and St Thomas’ NHS Foundation Trust, London, UK
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16
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Cromb D, Slator PJ, De La Fuente M, Price AN, Rutherford M, Egloff A, Counsell SJ, Hutter J. Assessing within-subject rates of change of placental MRI diffusion metrics in normal pregnancy. Magn Reson Med 2023; 90:1137-1150. [PMID: 37183839 PMCID: PMC10962570 DOI: 10.1002/mrm.29665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 03/14/2023] [Accepted: 03/22/2023] [Indexed: 05/16/2023]
Abstract
PURPOSE Studying placental development informs when development is abnormal. Most placental MRI studies are cross-sectional and do not study the extent of individual variability throughout pregnancy. We aimed to explore how diffusion MRI measures of placental function and microstructure vary in individual healthy pregnancies throughout gestation. METHODS Seventy-nine pregnant, low-risk participants (17 scanned twice and 62 scanned once) were included. T2 -weighted anatomical imaging and a combined multi-echo spin-echo diffusion-weighted sequence were acquired at 3 T. Combined diffusion-relaxometry models were performed using both aT 2 * $$ {\mathrm{T}}_2^{\ast } $$ -ADC and a bicompartmentalT 2 * $$ {\mathrm{T}}_2^{\ast } $$ -intravoxel-incoherent-motion (T 2 * IVIM $$ {\mathrm{T}}_2^{\ast}\;\mathrm{IVIM} $$ ) model fit. RESULTS There was a significant decline in placentalT 2 * $$ {\mathrm{T}}_2^{\ast } $$ and ADC (both P < 0.01) over gestation. These declines are consistent in individuals forT 2 * $$ {\mathrm{T}}_2^{\ast } $$ (covariance = -0.47), but not ADC (covariance = -1.04). TheT 2 * IVIM $$ {\mathrm{T}}_2^{\ast}\;\mathrm{IVIM} $$ model identified a consistent decline in individuals over gestation inT 2 * $$ {\mathrm{T}}_2^{\ast } $$ from both the perfusing and diffusing placental compartments, but not in ADC values from either. The placental perfusing compartment fraction increased over gestation (P = 0.0017), but this increase was not consistent in individuals (covariance = 2.57). CONCLUSION Whole placentalT 2 * $$ {\mathrm{T}}_2^{\ast } $$ and ADC values decrease over gestation, although onlyT 2 * $$ {\mathrm{T}}_2^{\ast } $$ values showed consistent trends within subjects. There was minimal individual variation in rates of change ofT 2 * $$ {\mathrm{T}}_2^{\ast } $$ values from perfusing and diffusing placental compartments, whereas trends in ADC values from these compartments were less consistent. These findings probably relate to the increased complexity of the bicompartmentalT 2 * IVIM $$ {\mathrm{T}}_2^{\ast}\;\mathrm{IVIM} $$ model, and differences in how different placental regions evolve at a microstructural level. These placental MRI metrics from low-risk pregnancies provide a useful benchmark for clinical cohorts.
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Affiliation(s)
- Daniel Cromb
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Paddy J. Slator
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
| | - Miguel De La Fuente
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Anthony N. Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Centre for Medical EngineeringSchool of Biomedical Engineering and Imaging Sciences, King's College LondonLondonUK
| | - Mary Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- MRC Centre for Neurodevelopmental DisordersKing's College LondonLondonUK
| | - Alexia Egloff
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Serena J. Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Centre for Medical EngineeringSchool of Biomedical Engineering and Imaging Sciences, King's College LondonLondonUK
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17
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Payette K, Uus A, Verdera JA, Zampieri CA, Hall M, Story L, Deprez M, Rutherford MA, Hajnal JV, Ourselin S, Tomi-Tricot R, Hutter J. An automated pipeline for quantitative T2* fetal body MRI and segmentation at low field. ArXiv 2023:arXiv:2308.04903v1. [PMID: 37608939 PMCID: PMC10441444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Fetal Magnetic Resonance Imaging at low field strengths is emerging as an exciting direction in perinatal health. Clinical low field (0.55T) scanners are beneficial for fetal imaging due to their reduced susceptibility-induced artefacts, increased T2* values, and wider bore (widening access for the increasingly obese pregnant population). However, the lack of standard automated image processing tools such as segmentation and reconstruction hampers wider clinical use. In this study, we introduce a semi-automatic pipeline using quantitative MRI for the fetal body at low field strength resulting in fast and detailed quantitative T2* relaxometry analysis of all major fetal body organs. Multi-echo dynamic sequences of the fetal body were acquired and reconstructed into a single high-resolution volume using deformable slice-to-volume reconstruction, generating both structural and quantitative T2* 3D volumes. A neural network trained using a semi-supervised approach was created to automatically segment these fetal body 3D volumes into ten different organs (resulting in dice values > 0.74 for 8 out of 10 organs). The T2* values revealed a strong relationship with GA in the lungs, liver, and kidney parenchyma (R2 >0.5). This pipeline was used successfully for a wide range of GAs (17-40 weeks), and is robust to motion artefacts. Low field fetal MRI can be used to perform advanced MRI analysis, and is a viable option for clinical scanning.
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Affiliation(s)
- Kelly Payette
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Alena Uus
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Jordina Aviles Verdera
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Carla Avena Zampieri
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Megan Hall
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Women & Children’s Health, King’s College London, London, UK: MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK
| | - Lisa Story
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Maria Deprez
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Mary A. Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Joseph V. Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Sebastien Ourselin
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Raphael Tomi-Tricot
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
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18
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Hutter J, Al-Wakeel A, Kyriakopoulou V, Matthew J, Story L, Rutherford M. Exploring the role of a time-efficient MRI assessment of the placenta and fetal brain in uncomplicated pregnancies and these complicated by placental insufficiency. Placenta 2023; 139:25-33. [PMID: 37295055 DOI: 10.1016/j.placenta.2023.05.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 02/24/2023] [Accepted: 05/20/2023] [Indexed: 06/12/2023]
Abstract
INTRODUCTION The development of placenta and fetal brain are intricately linked. Placental insufficiency is related to poor neonatal outcomes with impacts on neurodevelopment. This study sought to investigate whether simultaneous fast assessment of placental and fetal brain oxygenation using MRI T2* relaxometry can play a complementary role to US and Doppler US. METHODS This study is a retrospective case-control study with uncomplicated pregnancies (n = 99) and cases with placental insufficiency (PI) (n = 49). Participants underwent placental and fetal brain MRI and contemporaneous ultrasound imaging, resulting in quantitative assessment including a combined MRI score called Cerebro-placental-T2*-Ratio (CPTR). This was assessed in comparison with US-derived Cerebro-Placental-Ratio (CPR), placental histopathology, assessed using the Amsterdam criteria [1], and delivery details. RESULTS Pplacental and fetal brain T2* decreased with increasing gestational age in both low and high risk pregnancies and were corrected for gestational-age alsosignificantly decreased in PI. Both CPR and CPTR score were significantly correlated with gestational age at delivery for the entire cohort. CPTR was, however, also correlated independently with gestational age at delivery in the PI cohort. It furthermore showed a correlation to birth-weight-centile in healthy controls. DISCUSSION This study indicates that MR analysis of the placenta and brain may play a complementary role in the investigation of fetal development. The additional correlation to birth-weight-centile in controls may suggest a role in the determination of placental health even in healthy controls. To our knowledge, this is the first study assessing quantitatively both placental and fetal brain development over gestation in a large cohort of low and high risk pregnancies. Future larger prospective studies will include additional cohorts.
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Affiliation(s)
- Jana Hutter
- Centre for the Developing Brain, King's College London, UK; Centre for Medical Engineering, King's College London, UK.
| | - Ayman Al-Wakeel
- GKT School of Medical Education, King's College London, London, UK
| | - Vanessa Kyriakopoulou
- Centre for the Developing Brain, King's College London, UK; Centre for Medical Engineering, King's College London, UK
| | - Jacqueline Matthew
- Centre for the Developing Brain, King's College London, UK; Centre for Medical Engineering, King's College London, UK
| | - Lisa Story
- Centre for the Developing Brain, King's College London, UK; Institute for Women's and Children's Health, King's College London, UK; Fetal Medicine Unit, St Thomas' Hospital, London, UK
| | - Mary Rutherford
- Centre for the Developing Brain, King's College London, UK; Centre for Medical Engineering, King's College London, UK
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19
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Slator PJ, Verdera JA, Tomi-Tricot R, Hajnal JV, Alexander DC, Hutter J. Low-Field Combined Diffusion-Relaxation MRI for Mapping Placenta Structure and Function. medRxiv 2023:2023.06.06.23290983. [PMID: 37333076 PMCID: PMC10274995 DOI: 10.1101/2023.06.06.23290983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Purpose Demonstrating quantitative multi-parametric mapping in the placenta with combined T 2 ∗ -diffusion MRI at low-field (0.55T). Methods We present 57 placental MRI scans performed on a commercially available 0.55T scanner. We acquired the images using a combined T 2 ∗ -diffusion technique scan that simultaneously acquires multiple diffusion preparations and echo times. We processed the data to produce quantitative T 2 ∗ and diffusivity maps using a combined T 2 ∗ -ADC model. We compared the derived quantitative parameters across gestation in healthy controls and a cohort of clinical cases. Results Quantitative parameter maps closely resemble those from previous experiments at higher field strength, with similar trends in T 2 ∗ and ADC against gestational age observed. Conclusion Combined T 2 ∗ -diffusion placental MRI is reliably achievable at 0.55T. The advantages of lower field strength - such as cost, ease of deployment, increased accessibility and patient comfort due to the wider bore, and increased T 2 ∗ for larger dynamic ranges - can support the widespread roll out of placental MRI as an adjunct to ultrasound during pregnancy.
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Affiliation(s)
- Paddy J Slator
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | - Jordina Aviles Verdera
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Raphael Tomi-Tricot
- MR Research Collaborations, Siemens Healthcare Limited, Camberley, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Daniel C Alexander
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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20
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Avena-Zampieri CL, Hutter J, Deprez M, Payette K, Hall M, Uus A, Nanda S, Milan A, Seed PT, Rutherford M, Greenough A, Story L. Assessment of normal pulmonary development using functional magnetic resonance imaging techniques. Am J Obstet Gynecol MFM 2023; 5:100935. [PMID: 36933803 PMCID: PMC10711505 DOI: 10.1016/j.ajogmf.2023.100935] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 03/10/2023] [Accepted: 03/13/2023] [Indexed: 03/18/2023]
Abstract
BACKGROUND The mainstay of assessment of the fetal lungs in clinical practice is via evaluation of pulmonary size, primarily using 2D ultrasound and more recently with anatomical magnetic resonance imaging. The emergence of advanced magnetic resonance techniques such as T2* relaxometry in combination with the latest motion correction post-processing tools now facilitates assessment of the metabolic activity or perfusion of fetal pulmonary tissue in vivo. OBJECTIVE This study aimed to characterize normal pulmonary development using T2* relaxometry, accounting for fetal motion across gestation. METHODS Datasets from women with uncomplicated pregnancies that delivered at term, were analyzed. All subjects had undergone T2-weighted imaging and T2* relaxometry on a Phillips 3T magnetic resonance imaging system antenatally. T2* relaxometry of the fetal thorax was performed using a gradient echo single-shot echo planar imaging sequence. Following correction for fetal motion using slice-to-volume reconstruction, T2* maps were generated using in-house pipelines. Lungs were manually segmented and mean T2* values calculated for the right and left lungs individually, and for both lungs combined. Lung volumes were generated from the segmented images, and the right and left lungs, as well as both lungs combined were assessed. RESULTS Eighty-seven datasets were suitable for analysis. The mean gestation at scan was 29.9±4.3 weeks (range: 20.6-38.3) and mean gestation at delivery was 40±1.2 weeks (range: 37.1-42.4). Mean T2* values of the lungs increased over gestation for right and left lungs individually and for both lungs assessed together (P=.003; P=.04; P=.003, respectively). Right, left, and total lung volumes were also strongly correlated with increasing gestational age (P<.001 in all cases). CONCLUSION This large study assessed developing lungs using T2* imaging across a wide gestational age range. Mean T2* values increased with gestational age, which may reflect increasing perfusion and metabolic requirements and alterations in tissue composition as gestation advances. In the future, evaluation of findings in fetuses with conditions known to be associated with pulmonary morbidity may lead to enhanced prognostication antenatally, consequently improving counseling and perinatal care planning.
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Affiliation(s)
- Carla L Avena-Zampieri
- Department of Women and Children's Health, King's College London, London, United Kingdom (XX Avena-Zampieri, XX Hall, XX Seed, XX Greenough, and XX Story); Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom (Ms Avena-Zampieri, Dr Hutter, Mr Deprez, Ms Payette, Dr Hall, Ms Uus, Prof Rutherford, and Dr Story).
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom (Ms Avena-Zampieri, Dr Hutter, Mr Deprez, Ms Payette, Dr Hall, Ms Uus, Prof Rutherford, and Dr Story)
| | - Maria Deprez
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom (Ms Avena-Zampieri, Dr Hutter, Mr Deprez, Ms Payette, Dr Hall, Ms Uus, Prof Rutherford, and Dr Story); Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom (Ms Deprez, Ms Payette, and Ms Uus)
| | - Kelly Payette
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom (Ms Avena-Zampieri, Dr Hutter, Mr Deprez, Ms Payette, Dr Hall, Ms Uus, Prof Rutherford, and Dr Story); Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom (Ms Deprez, Ms Payette, and Ms Uus)
| | - Megan Hall
- Department of Women and Children's Health, King's College London, London, United Kingdom (XX Avena-Zampieri, XX Hall, XX Seed, XX Greenough, and XX Story); Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom (Ms Avena-Zampieri, Dr Hutter, Mr Deprez, Ms Payette, Dr Hall, Ms Uus, Prof Rutherford, and Dr Story); Fetal Medicine Unit, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom (Dr Hall, Dr Nanda, and Dr Story)
| | - Alena Uus
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom (Ms Avena-Zampieri, Dr Hutter, Mr Deprez, Ms Payette, Dr Hall, Ms Uus, Prof Rutherford, and Dr Story); Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom (Ms Deprez, Ms Payette, and Ms Uus)
| | - Surabhi Nanda
- Fetal Medicine Unit, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom (Dr Hall, Dr Nanda, and Dr Story)
| | - Anna Milan
- Neonatal Unit, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom (Dr Milan)
| | - Paul T Seed
- Department of Women and Children's Health, King's College London, London, United Kingdom (XX Avena-Zampieri, XX Hall, XX Seed, XX Greenough, and XX Story)
| | - Mary Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom (Ms Avena-Zampieri, Dr Hutter, Mr Deprez, Ms Payette, Dr Hall, Ms Uus, Prof Rutherford, and Dr Story)
| | - Anne Greenough
- Department of Women and Children's Health, King's College London, London, United Kingdom (XX Avena-Zampieri, XX Hall, XX Seed, XX Greenough, and XX Story); Neonatal Unit, King's College Hospital, London, United Kingdom (Prof Greenough); National Institute for Health and Care Research Biomedical Research Centre based at Guy's & St Thomas NHS Foundation Trusts and King's College London, London, United Kingdom (Prof Greenough)
| | - Lisa Story
- Department of Women and Children's Health, King's College London, London, United Kingdom (XX Avena-Zampieri, XX Hall, XX Seed, XX Greenough, and XX Story); Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom (Ms Avena-Zampieri, Dr Hutter, Mr Deprez, Ms Payette, Dr Hall, Ms Uus, Prof Rutherford, and Dr Story); Fetal Medicine Unit, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom (Dr Hall, Dr Nanda, and Dr Story)
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21
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Uus AU, Kyriakopoulou V, Makropoulos A, Fukami-Gartner A, Cromb D, Davidson A, Cordero-Grande L, Price AN, Grigorescu I, Williams LZJ, Robinson EC, Lloyd D, Pushparajah K, Story L, Hutter J, Counsell SJ, Edwards AD, Rutherford MA, Hajnal JV, Deprez M. BOUNTI: Brain vOlumetry and aUtomated parcellatioN for 3D feTal MRI. bioRxiv 2023:2023.04.18.537347. [PMID: 37131820 PMCID: PMC10153133 DOI: 10.1101/2023.04.18.537347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Fetal MRI is widely used for quantitative brain volumetry studies. However, currently, there is a lack of universally accepted protocols for fetal brain parcellation and segmentation. Published clinical studies tend to use different segmentation approaches that also reportedly require significant amounts of time-consuming manual refinement. In this work, we propose to address this challenge by developing a new robust deep learning-based fetal brain segmentation pipeline for 3D T2w motion corrected brain images. At first, we defined a new refined brain tissue parcellation protocol with 19 regions-of-interest using the new fetal brain MRI atlas from the Developing Human Connectome Project. This protocol design was based on evidence from histological brain atlases, clear visibility of the structures in individual subject 3D T2w images and the clinical relevance to quantitative studies. It was then used as a basis for developing an automated deep learning brain tissue parcellation pipeline trained on 360 fetal MRI datasets with different acquisition parameters using semi-supervised approach with manually refined labels propagated from the atlas. The pipeline demonstrated robust performance for different acquisition protocols and GA ranges. Analysis of tissue volumetry for 390 normal participants (21-38 weeks gestational age range), scanned with three different acquisition protocols, did not reveal significant differences for major structures in the growth charts. Only minor errors were present in < 15% of cases thus significantly reducing the need for manual refinement. In addition, quantitative comparison between 65 fetuses with ventriculomegaly and 60 normal control cases were in agreement with the findings reported in our earlier work based on manual segmentations. These preliminary results support the feasibility of the proposed atlas-based deep learning approach for large-scale volumetric analysis. The created fetal brain volumetry centiles and a docker with the proposed pipeline are publicly available online at https://hub.docker.com/r/fetalsvrtk/segmentation (tag brain_bounti_tissue).
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Affiliation(s)
- Alena U Uus
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | | | | | | | - Daniel Cromb
- Centre for the Developing Brain, King's College London, London, UK
| | - Alice Davidson
- Centre for the Developing Brain, King's College London, London, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, King's College London, London, UK
- Biomedical Image Technologies, ETSI Telecomunicacion, Universidad Politécnica de Madrid and CIBER-BBN, ISCII, Madrid, Spain
| | - Anthony N Price
- Centre for the Developing Brain, King's College London, London, UK
| | - Irina Grigorescu
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Logan Z J Williams
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Emma C Robinson
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - David Lloyd
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Department of Congenital Heart Disease, Evelina London Children's Hospital, London, UK
| | - Kuberan Pushparajah
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Department of Congenital Heart Disease, Evelina London Children's Hospital, London, UK
| | - Lisa Story
- Centre for the Developing Brain, King's College London, London, UK
| | - Jana Hutter
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | | | - A David Edwards
- Centre for the Developing Brain, King's College London, London, UK
| | | | - Joseph V Hajnal
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Maria Deprez
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
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22
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Hall M, de Marvao A, Schweitzer R, Cromb D, Colford K, Jandu P, O'Regan DP, Ho A, Price A, Chappell LC, Rutherford MA, Story L, Lamata P, Hutter J. Characterisation of placental, fetal brain and maternal cardiac structure and function in pre-eclampsia using MRI. medRxiv 2023:2023.04.24.23289069. [PMID: 37163073 PMCID: PMC10168502 DOI: 10.1101/2023.04.24.23289069] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Background Pre-eclampsia is a multiorgan disease of pregnancy that has short- and long-term implications for the woman and fetus, whose immediate impact is poorly understood. We present a novel multi-system approach to MRI investigation of pre-eclampsia, with acquisition of maternal cardiac, placental, and fetal brain anatomical and functional imaging. Methods A prospective study was carried out recruiting pregnant women with pre-eclampsia, chronic hypertension, or no medical complications, and a non-pregnant female cohort. All women underwent a cardiac MRI, and pregnant women underwent a fetal-placental MRI. Cardiac analysis for structural, morphological and flow data was undertaken; placenta and fetal brain volumetric and T2* data were obtained. All results were corrected for gestational age. Results Seventy-eight MRIs were obtained during pregnancy. Pregnancies affected by pre-eclampsia demonstrated lower placental and fetal brain T2*. Within the pre-eclampsia group, three placental T2* results were within the normal range, these were the only cases with normal placental histopathology. Similarly, three fetal brain T2* results were within the normal range; these cases had no evidence of cerebral redistribution on fetal Dopplers. Cardiac MRI analysis demonstrated higher left ventricular mass in pre-eclampsia with 3D modelling revealing additional specific characteristics of eccentricity and outflow track remodelling. Conclusions We present the first holistic assessment of the immediate implications of pre-eclampsia on the placenta, maternal heart, and fetal brain. As well as having potential clinical implications for the risk-stratification and management of women with pre-eclampsia, this gives an insight into disease mechanism.
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Affiliation(s)
- Megan Hall
- Department of Women and Children’s Health, King’s College London, UK
- Centre for the Developing Brain, King’s College London, UK
| | - Antonio de Marvao
- Department of Women and Children’s Health, King’s College London, UK
- School of Cardiovascular Medicine, King’s College London, UK
- MRC London Institute of Medical Sciences, Imperial College London, UK
| | - Ronny Schweitzer
- School of Cardiovascular Medicine, King’s College London, UK
- MRC London Institute of Medical Sciences, Imperial College London, UK
| | - Daniel Cromb
- Centre for the Developing Brain, King’s College London, UK
| | | | - Priya Jandu
- GKT School of Medical Education, King’s College London, UK
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, UK
| | - Alison Ho
- Department of Women and Children’s Health, King’s College London, UK
- Centre for the Developing Brain, King’s College London, UK
| | - Anthony Price
- Centre for the Developing Brain, King’s College London, UK
- Centre for Medical Engineering, King’s College London, UK
| | - Lucy C. Chappell
- Department of Women and Children’s Health, King’s College London, UK
| | | | - Lisa Story
- Department of Women and Children’s Health, King’s College London, UK
- Centre for the Developing Brain, King’s College London, UK
| | - Pablo Lamata
- Centre for Medical Engineering, King’s College London, UK
| | - Jana Hutter
- Centre for the Developing Brain, King’s College London, UK
- Centre for Medical Engineering, King’s College London, UK
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23
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Wilson S, Pietsch M, Cordero-Grande L, Christiaens D, Uus A, Karolis VR, Kyriakopoulou V, Colford K, Price AN, Hutter J, Rutherford MA, Hughes EJ, Counsell SJ, Tournier JD, Hajnal JV, Edwards AD, O’Muircheartaigh J, Arichi T. Spatiotemporal tissue maturation of thalamocortical pathways in the human fetal brain. eLife 2023; 12:e83727. [PMID: 37010273 PMCID: PMC10125021 DOI: 10.7554/elife.83727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 03/31/2023] [Indexed: 04/04/2023] Open
Abstract
The development of connectivity between the thalamus and maturing cortex is a fundamental process in the second half of human gestation, establishing the neural circuits that are the basis for several important brain functions. In this study, we acquired high-resolution in utero diffusion magnetic resonance imaging (MRI) from 140 fetuses as part of the Developing Human Connectome Project, to examine the emergence of thalamocortical white matter over the second to third trimester. We delineate developing thalamocortical pathways and parcellate the fetal thalamus according to its cortical connectivity using diffusion tractography. We then quantify microstructural tissue components along the tracts in fetal compartments that are critical substrates for white matter maturation, such as the subplate and intermediate zone. We identify patterns of change in the diffusion metrics that reflect critical neurobiological transitions occurring in the second to third trimester, such as the disassembly of radial glial scaffolding and the lamination of the cortical plate. These maturational trajectories of MR signal in transient fetal compartments provide a normative reference to complement histological knowledge, facilitating future studies to establish how developmental disruptions in these regions contribute to pathophysiology.
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Affiliation(s)
- Siân Wilson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
- Centre for Neurodevelopmental Disorders, King’s College LondonLondonUnited Kingdom
| | - Maximilian Pietsch
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de MadridMadridSpain
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)MadridSpain
| | - Daan Christiaens
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
- Department of Electrical Engineering (ESAT/PSI), Katholieke Universiteit LeuvenLeuvenBelgium
| | - Alena Uus
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas' HospitalLondonUnited Kingdom
| | - Vyacheslav R Karolis
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Vanessa Kyriakopoulou
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Kathleen Colford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Emer J Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
- Centre for Neurodevelopmental Disorders, King’s College LondonLondonUnited Kingdom
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
- Centre for Neurodevelopmental Disorders, King’s College LondonLondonUnited Kingdom
- Department of Forensic and Neurodevelopmental Sciences, King’s College LondonLondonUnited Kingdom
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College LondonLondonUnited Kingdom
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
- Centre for Neurodevelopmental Disorders, King’s College LondonLondonUnited Kingdom
- Children’s Neurosciences, Evelina London Children’s Hospital, Guy’s and St Thomas’ NHS Foundation TrustLondonUnited Kingdom
- Department of Bioengineering, Imperial College LondonLondonUnited Kingdom
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24
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Bos B, Barratt B, Batalle D, Gale-Grant O, Hughes EJ, Beevers S, Cordero-Grande L, Price AN, Hutter J, Hajnal JV, Kelly FJ, David Edwards A, Counsell SJ. Prenatal exposure to air pollution is associated with structural changes in the neonatal brain. Environ Int 2023; 174:107921. [PMID: 37058974 PMCID: PMC10410199 DOI: 10.1016/j.envint.2023.107921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/23/2023] [Accepted: 04/04/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND Prenatal exposure to air pollution is associated with adverse neurologic consequences in childhood. However, the relationship between in utero exposure to air pollution and neonatal brain development is unclear. METHODS We modelled maternal exposure to nitrogen dioxide (NO2) and particulate matter (PM2.5 and PM10) at postcode level between date of conception to date of birth and studied the effect of prenatal air pollution exposure on neonatal brain morphology in 469 (207 male) healthy neonates, with gestational age of ≥36 weeks. Infants underwent MR neuroimaging at 3 Tesla at 41.29 (36.71-45.14) weeks post-menstrual age (PMA) as part of the developing human connectome project (dHCP). Single pollutant linear regression and canonical correlation analysis (CCA) were performed to assess the relationship between air pollution and brain morphology, adjusting for confounders and correcting for false discovery rate. RESULTS Higher exposure to PM10 and lower exposure to NO2 was strongly canonically correlated to a larger relative ventricular volume, and moderately associated with larger relative size of the cerebellum. Modest associations were detected with higher exposure to PM10 and lower exposure to NO2 and smaller relative cortical grey matter and amygdala and hippocampus, and larger relaive brainstem and extracerebral CSF volume. No associations were found with white matter or deep grey nuclei volume. CONCLUSIONS Our findings show that prenatal exposure to air pollution is associated with altered brain morphometry in the neonatal period, albeit with opposing results for NO2 and PM10. This finding provides further evidence that reducing levels of maternal exposure to particulate matter during pregnancy should be a public health priority and highlights the importance of understanding the impacts of air pollution on this critical development window.
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Affiliation(s)
- Brendan Bos
- MRC Centre for Environment and Health, Imperial College London, UK
| | - Ben Barratt
- MRC Centre for Environment and Health, Imperial College London, UK
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK; Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Oliver Gale-Grant
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK; Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Emer J Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Sean Beevers
- MRC Centre for Environment and Health, Imperial College London, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Frank J Kelly
- MRC Centre for Environment and Health, Imperial College London, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.
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25
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Hutter J, Slator PJ, Avena Zampieri C, Hall M, Rutherford M, Story L. Multi-modal MRI reveals changes in placental function following preterm premature rupture of membranes. Magn Reson Med 2023; 89:1151-1159. [PMID: 36255151 PMCID: PMC10091779 DOI: 10.1002/mrm.29483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 09/18/2022] [Accepted: 09/20/2022] [Indexed: 02/02/2023]
Abstract
PURPOSE Preterm premature rupture of membranes complicates up to 40% of premature deliveries. Fetal infection may occur in the absence of maternal symptoms, delaying diagnosis and increasing morbidity and mortality. A noninvasive antenatal assessment of early signs of placental inflammation is therefore urgently required. METHODS Sixteen women with preterm premature rupture of membranes < 34 weeks gestation and 60 women with uncomplicated pregnancies were prospectively recruited. A modified diffusion-weighted spin-echo single shot EPI sequence with a diffusion preparation acquiring 264 unique parameter combinations in < 9 min was obtained on a clinical 3 Tesla MRI scanner. The data was fitted to a 2-compartment T 2 * $$ {\mathrm{T}}_2^{\ast } $$ -intravoxel incoherent motion model comprising fast and slowly circulating fluid pools to obtain quantitative information on perfusion, density, and tissue composition. Z values were calculated, and correlation with time from between the rupture of membranes and the scan, gestational age at delivery, and time between scan and delivery assessed. RESULTS Placental T 2 * $$ {\mathrm{T}}_2^{\ast } $$ was significantly reduced in preterm premature rupture of membranes, and the 2-compartmental model demonstrated that this decline is mainly linked to the perfusion component observed in the placental parenchyma. Multi-modal MRI measurement of placental function is linked to gestational age at delivery and time from membrane rupture. CONCLUSION More complex models and data acquisition can potentially improve fitting of the underlying etiology of preterm birth compared with individual single-contrast models and contribute to additional insights in the future. This will need validation in larger cohorts. A multi-modal MRI acquisition between rupture of the membranes and delivery can be used to measure placental function and is linked to gestational age at delivery.
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Affiliation(s)
- Jana Hutter
- Centre for the Developing Brain, King's College London, London, United Kingdom.,Centre for Medical Engineering, King's College London, London, United Kingdom
| | | | - Carla Avena Zampieri
- Centre for the Developing Brain, King's College London, London, United Kingdom.,Centre for Medical Engineering, King's College London, London, United Kingdom
| | - Megan Hall
- Centre for the Developing Brain, King's College London, London, United Kingdom.,Institute for Women's and Children's Health, King's College London, London, United Kingdom.,Fetal Medicine Unit, St Thomas' Hospital, London, United Kingdom
| | - Mary Rutherford
- Centre for the Developing Brain, King's College London, London, United Kingdom.,Centre for Medical Engineering, King's College London, London, United Kingdom
| | - Lisa Story
- Centre for the Developing Brain, King's College London, London, United Kingdom.,Institute for Women's and Children's Health, King's College London, London, United Kingdom.,Fetal Medicine Unit, St Thomas' Hospital, London, United Kingdom
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26
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Avena-Zampieri CL, Hutter J, Rutherford M, Milan A, Hall M, Egloff A, Lloyd DFA, Nanda S, Greenough A, Story L. Assessment of the fetal lungs in utero. Am J Obstet Gynecol MFM 2022; 4:100693. [PMID: 35858660 PMCID: PMC9811184 DOI: 10.1016/j.ajogmf.2022.100693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 07/12/2022] [Accepted: 07/12/2022] [Indexed: 01/07/2023]
Abstract
Antenatal diagnosis of abnormal pulmonary development has improved significantly over recent years because of progress in imaging techniques. Two-dimensional ultrasound is the mainstay of investigation of pulmonary pathology during pregnancy, providing good prognostication in conditions such as congenital diaphragmatic hernia; however, it is less validated in other high-risk groups such as those with congenital pulmonary airway malformation or preterm premature rupture of membranes. Three-dimensional assessment of lung volume and size is now possible using ultrasound or magnetic resonance imaging; however, the use of these techniques is still limited because of unpredictable fetal motion, and such tools have also been inadequately validated in high-risk populations other than those with congenital diaphragmatic hernia. The advent of advanced, functional magnetic resonance imaging techniques such as diffusion and T2* imaging, and the development of postprocessing pipelines that facilitate motion correction, have enabled not only more accurate evaluation of pulmonary size, but also assessment of tissue microstructure and perfusion. In the future, fetal magnetic resonance imaging may have an increasing role in the prognostication of pulmonary abnormalities and in monitoring current and future antenatal therapies to enhance lung development. This review aims to examine the current imaging methods available for assessment of antenatal lung development and to outline possible future directions.
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Affiliation(s)
- Carla L Avena-Zampieri
- Department of Women and Children's Health, King's College London, London, United Kingdom; Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Mary Rutherford
- Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Anna Milan
- Neonatal Unit, Guy's and St Thomas' National Health Service Foundation Trust, London, United Kingdom
| | - Megan Hall
- Department of Women and Children's Health, King's College London, London, United Kingdom; Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Alexia Egloff
- Centre for the Developing Brain, King's College London, London, United Kingdom
| | - David F A Lloyd
- Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Surabhi Nanda
- Fetal Medicine Unit, Guy's and St Thomas' National Health Service Foundation Trust, London, United Kingdom
| | - Anne Greenough
- Department of Women and Children's Health, King's College London, London, United Kingdom; Neonatal Unit, King's College Hospital, London, United Kingdom; Asthma UK Centre in Allergic Mechanisms of Asthma, King's College London, London, United Kingdom; National Institute for Health and Care Research Biomedical Research Centre, Guy's & St Thomas National Health Service Foundation Trust and King's College London, London, United Kingdom
| | - Lisa Story
- Department of Women and Children's Health, King's College London, London, United Kingdom; Centre for the Developing Brain, King's College London, London, United Kingdom; Fetal Medicine Unit, Guy's and St Thomas' National Health Service Foundation Trust, London, United Kingdom.
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27
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Bouhrara M, Hutter J, Benjamini D. Editorial: Capturing Biological Complexity and Heterogeneity Using Multidimensional MRI. Front Phys 2022; 10:950928. [PMID: 38031630 PMCID: PMC10686314 DOI: 10.3389/fphy.2022.950928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Affiliation(s)
- Mustapha Bouhrara
- Magnetic Resonance Physics of Aging and Dementia Unit, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States
| | - Jana Hutter
- Centre for the Developing Brain, King’s College London, London, United Kingdom
- Biomedical Engineering Department, King’s College London, London, United Kingdom
| | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States
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28
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Hall M, Hutter J, Suff N, Zampieri CA, Tribe RM, Shennan A, Rutherford M, Story L. Antenatal diagnosis of chorioamnionitis: A review of the potential role of fetal and placental imaging. Prenat Diagn 2022; 42:1049-1058. [PMID: 35670265 PMCID: PMC9543023 DOI: 10.1002/pd.6188] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 05/09/2022] [Accepted: 05/17/2022] [Indexed: 11/12/2022]
Abstract
Chorioamnionitis is present in up to 70% of spontaneous preterm births. It is defined as an acute inflammation of the chorion, with or without involvement of the amnion, and is evidence of a maternal immunological response to infection. A fetal inflammatory response can coexist and is diagnosed on placental histopathology postnatally. Fetal inflammatory response syndrome (FIRS) is associated with poorer fetal and neonatal outcomes. The only antenatal diagnostic test is amniocentesis which carries risks of miscarriage or preterm birth. Imaging of the fetal immune system, in particular the thymus and the spleen, and the placenta may give valuable information antenatally regarding the diagnosis of fetal inflammatory response. While ultrasound is largely limited to structural information, MRI can complement this with functional information that may provide insight into the metabolic activities of the fetal immune system and placenta. This review discusses fetal and placental imaging in pregnancies complicated by chorioamnionitis and their potential future use in achieving non-invasive antenatal diagnosis. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Megan Hall
- Department of Women and Children's Health, St Thomas' Hospital, King's College London, London, UK.,Centre for the Developing Brain, St Thomas' Hospital, King's College London, London, UK
| | - Jana Hutter
- Centre for the Developing Brain, St Thomas' Hospital, King's College London, London, UK
| | - Natalie Suff
- Department of Women and Children's Health, St Thomas' Hospital, King's College London, London, UK
| | - Carla Avena Zampieri
- Centre for the Developing Brain, St Thomas' Hospital, King's College London, London, UK
| | - Rachel M Tribe
- Department of Women and Children's Health, St Thomas' Hospital, King's College London, London, UK
| | - Andrew Shennan
- Department of Women and Children's Health, St Thomas' Hospital, King's College London, London, UK
| | - Mary Rutherford
- Centre for the Developing Brain, St Thomas' Hospital, King's College London, London, UK
| | - Lisa Story
- Department of Women and Children's Health, St Thomas' Hospital, King's College London, London, UK.,Centre for the Developing Brain, St Thomas' Hospital, King's College London, London, UK
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29
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Ciarrusta J, Christiaens D, Fitzgibbon SP, Dimitrova R, Hutter J, Hughes E, Duff E, Price AN, Cordero-Grande L, Tournier JD, Rueckert D, Hajnal JV, Arichi T, McAlonan G, Edwards AD, Batalle D. The developing brain structural and functional connectome fingerprint. Dev Cogn Neurosci 2022; 55:101117. [PMID: 35662682 PMCID: PMC9344310 DOI: 10.1016/j.dcn.2022.101117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/14/2022] [Accepted: 05/17/2022] [Indexed: 11/03/2022] Open
Abstract
In the mature brain, structural and functional 'fingerprints' of brain connectivity can be used to identify the uniqueness of an individual. However, whether the characteristics that make a given brain distinguishable from others already exist at birth remains unknown. Here, we used neuroimaging data from the developing Human Connectome Project (dHCP) of preterm born neonates who were scanned twice during the perinatal period to assess the developing brain fingerprint. We found that 62% of the participants could be identified based on the congruence of the later structural connectome to the initial connectivity matrix derived from the earlier timepoint. In contrast, similarity between functional connectomes of the same subject at different time points was low. Only 10% of the participants showed greater self-similarity in comparison to self-to-other-similarity for the functional connectome. These results suggest that structural connectivity is more stable in early life and can represent a potential connectome fingerprint of the individual: a relatively stable structural connectome appears to support a changing functional connectome at a time when neonates must rapidly acquire new skills to adapt to their new environment.
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Affiliation(s)
- Judit Ciarrusta
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Center for Brain and Cognition (CBC), Universitat Pompeu Fabra, Barcelona, Spain
| | - Daan Christiaens
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Sean P Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Ralica Dimitrova
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Eugene Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Paediatric Neuroimaging Group, Department of Paediatrics, University of Oxford, UK
| | - Anthony N Price
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Department of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom; Children's Neurosciences, Evelina London Children's Hospital, Guy's and St Thomas' NHS Trust, London, United Kingdom
| | - Grainne McAlonan
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom
| | - Dafnis Batalle
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
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30
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Edwards AD, Rueckert D, Smith SM, Abo Seada S, Alansary A, Almalbis J, Allsop J, Andersson J, Arichi T, Arulkumaran S, Bastiani M, Batalle D, Baxter L, Bozek J, Braithwaite E, Brandon J, Carney O, Chew A, Christiaens D, Chung R, Colford K, Cordero-Grande L, Counsell SJ, Cullen H, Cupitt J, Curtis C, Davidson A, Deprez M, Dillon L, Dimitrakopoulou K, Dimitrova R, Duff E, Falconer S, Farahibozorg SR, Fitzgibbon SP, Gao J, Gaspar A, Harper N, Harrison SJ, Hughes EJ, Hutter J, Jenkinson M, Jbabdi S, Jones E, Karolis V, Kyriakopoulou V, Lenz G, Makropoulos A, Malik S, Mason L, Mortari F, Nosarti C, Nunes RG, O’Keeffe C, O’Muircheartaigh J, Patel H, Passerat-Palmbach J, Pietsch M, Price AN, Robinson EC, Rutherford MA, Schuh A, Sotiropoulos S, Steinweg J, Teixeira RPAG, Tenev T, Tournier JD, Tusor N, Uus A, Vecchiato K, Williams LZJ, Wright R, Wurie J, Hajnal JV. The Developing Human Connectome Project Neonatal Data Release. Front Neurosci 2022; 16:886772. [PMID: 35677357 PMCID: PMC9169090 DOI: 10.3389/fnins.2022.886772] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/19/2022] [Indexed: 11/24/2022] Open
Abstract
The Developing Human Connectome Project has created a large open science resource which provides researchers with data for investigating typical and atypical brain development across the perinatal period. It has collected 1228 multimodal magnetic resonance imaging (MRI) brain datasets from 1173 fetal and/or neonatal participants, together with collateral demographic, clinical, family, neurocognitive and genomic data from 1173 participants, together with collateral demographic, clinical, family, neurocognitive and genomic data. All subjects were studied in utero and/or soon after birth on a single MRI scanner using specially developed scanning sequences which included novel motion-tolerant imaging methods. Imaging data are complemented by rich demographic, clinical, neurodevelopmental, and genomic information. The project is now releasing a large set of neonatal data; fetal data will be described and released separately. This release includes scans from 783 infants of whom: 583 were healthy infants born at term; as well as preterm infants; and infants at high risk of atypical neurocognitive development. Many infants were imaged more than once to provide longitudinal data, and the total number of datasets being released is 887. We now describe the dHCP image acquisition and processing protocols, summarize the available imaging and collateral data, and provide information on how the data can be accessed.
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Affiliation(s)
- A. David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
- Institute for AI and Informatics in Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stephen M. Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Samy Abo Seada
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Amir Alansary
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Jennifer Almalbis
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Joanna Allsop
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jesper Andersson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Sophie Arulkumaran
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Luke Baxter
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jelena Bozek
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Eleanor Braithwaite
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, United Kingdom
| | - Jacqueline Brandon
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Olivia Carney
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Andrew Chew
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Raymond Chung
- BioResource Centre, NIHR Biomedical Research Centre, South London and Maudsley NHS Trust, London, United Kingdom
| | - Kathleen Colford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Serena J. Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Harriet Cullen
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King’s College London, London, United Kingdom
| | - John Cupitt
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Charles Curtis
- BioResource Centre, NIHR Biomedical Research Centre, South London and Maudsley NHS Trust, London, United Kingdom
| | - Alice Davidson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Maria Deprez
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Louise Dillon
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Konstantina Dimitrakopoulou
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Translational Bioinformatics Platform, NIHR Biomedical Research Centre, Guy’s and St. Thomas’ NHS Foundation Trust and King’s College London, London, United Kingdom
| | - Ralica Dimitrova
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Eugene Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Shona Falconer
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Seyedeh-Rezvan Farahibozorg
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Sean P. Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jianliang Gao
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Andreia Gaspar
- Institute for Systems and Robotics (ISR-Lisboa)/LaRSyS, Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Nicholas Harper
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Sam J. Harrison
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Emer J. Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Emily Jones
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, United Kingdom
| | - Vyacheslav Karolis
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Vanessa Kyriakopoulou
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Gregor Lenz
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Antonios Makropoulos
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Shaihan Malik
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Luke Mason
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, United Kingdom
| | - Filippo Mortari
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Chiara Nosarti
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Rita G. Nunes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Institute for Systems and Robotics (ISR-Lisboa)/LaRSyS, Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Camilla O’Keeffe
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Hamel Patel
- BioResource Centre, NIHR Biomedical Research Centre, South London and Maudsley NHS Trust, London, United Kingdom
| | - Jonathan Passerat-Palmbach
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Maximillian Pietsch
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Anthony N. Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Emma C. Robinson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Mary A. Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Stamatios Sotiropoulos
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Johannes Steinweg
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Rui Pedro Azeredo Gomes Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Tencho Tenev
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Nora Tusor
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Alena Uus
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Logan Z. J. Williams
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Robert Wright
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Julia Wurie
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Joseph V. Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
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31
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Taoudi-Benchekroun Y, Christiaens D, Grigorescu I, Gale-Grant O, Schuh A, Pietsch M, Chew A, Harper N, Falconer S, Poppe T, Hughes E, Hutter J, Price AN, Tournier JD, Cordero-Grande L, Counsell SJ, Rueckert D, Arichi T, Hajnal JV, Edwards AD, Deprez M, Batalle D. Predicting age and clinical risk from the neonatal connectome. Neuroimage 2022; 257:119319. [PMID: 35589001 DOI: 10.1016/j.neuroimage.2022.119319] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 02/28/2022] [Accepted: 05/12/2022] [Indexed: 12/12/2022] Open
Abstract
The development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. Diffusion MRI allows the characterisation of subtle inter-individual differences in structural brain connectivity. Individual brain connectivity maps (connectomes) are by nature high in dimensionality and complex to interpret. Machine learning methods are a powerful tool to uncover properties of the connectome which are not readily visible and can give us clues as to how and why individual developmental trajectories differ. In this manuscript we used Deep Neural Networks and Random Forests to predict demographic and neurodevelopmental characteristics from neonatal structural connectomes in a large sample of babies (n = 524) from the developing Human Connectome Project. We achieved an accurate prediction of post menstrual age (PMA) at scan in term-born infants (mean absolute error (MAE) = 0.72 weeks, r = 0.83 and p<0.001). We also achieved good accuracy when predicting gestational age at birth in a cohort of term and preterm babies scanned at term equivalent age (MAE = 2.21 weeks, r = 0.82, p<0.001). We subsequently used sensitivity analysis to obtain feature relevance from our prediction models, with the most important connections for prediction of PMA and GA found to predominantly involve frontal and temporal regions, thalami, and basal ganglia. From our models of PMA at scan for infants born at term, we computed a brain maturation index (predicted age minus actual age) of individual preterm neonates and found a significant correlation between this index and motor outcome at 18 months corrected age. Our results demonstrate the applicability of machine learning techniques in analyses of the neonatal connectome and suggest that a neural substrate of brain maturation with implications for future neurodevelopment is detectable at term equivalent age from the neonatal connectome.
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Affiliation(s)
- Yassine Taoudi-Benchekroun
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Irina Grigorescu
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Oliver Gale-Grant
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Maximilian Pietsch
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Andrew Chew
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Nicholas Harper
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Shona Falconer
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Tanya Poppe
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
| | - Serena J Counsell
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Institute for Artificial Intelligence and Informatics in Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Department of Bioengineering, Imperial College London, London, United Kingdom; Children's Neurosciences, Evelina London Children's Hospital, Guy's and St Thomas' NHS Trust, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom
| | - Maria Deprez
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
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32
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Colford K, Price AN, Sigurdardottir J, Fotaki A, Steinweg J, Story L, Ho A, Chappell LC, Hajnal JV, Rutherford M, Pushparajah K, Lamata P, Hutter J. Cardiac and placental imaging (CARP) in pregnancy to assess aetiology of preeclampsia. Placenta 2022; 122:46-55. [PMID: 35430505 PMCID: PMC9810538 DOI: 10.1016/j.placenta.2022.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 02/12/2022] [Accepted: 03/01/2022] [Indexed: 01/07/2023]
Abstract
INTRODUCTION The CARP study aims to investigate placental function, cardiac function and fetal growth comprehensively during pregnancy, a time of maximal cardiac stress, to work towards disentangling the complex cardiac and placental interactions presenting in the aetiology of pre-eclampsia as well as predicting maternal Cardiovascular Disease (CVD) risk in later life. BACKGROUND The involvement of the cardiovascular system in pre-eclampsia, one of the most serious complications of pregnancy, is evident. While the manifestations of pre-eclampsia during pregnancy (high blood pressure, multi-organ disease, and placental dysfunction) resolve after delivery, a lifelong elevated CVD risk remains. METHOD An assessment including both cardiac and placental Magnetic Resonance Imaging (MRI) optimised for use in pregnancy and bespoke to the expected changes was developed. Simultaneous structural and functional MRI data from the placenta, the heart and the fetus were obtained in a total of 32 pregnant women (gestational ages from 18.1 to 37.5 weeks), including uncomplicated pregnancies and five cases with early onset pre-eclampsia. RESULTS The achieved comprehensive MR acquisition was able to demonstrate a phenotype associated with pre-eclampsia linking both placental and cardiac factors, reduced mean T2* (p < 0.005), increased heterogeneity (p < 0.005) and a trend towards an increase in cardiac work, larger average mass (109.4 vs 93.65 gr), wall thickness (7.0 vs 6.4 mm), blood pool volume (135.7 vs 127.48 mL) and mass to volume ratio (0.82 vs 0.75). The cardiac output in the controls was, controlling for gestational age, positively correlated with placental volume (p < 0.05). DISCUSSION The CARP study constitutes the first joint assessment of functional and structural properties of the cardiac system and the placenta during pregnancy. Early indications of cardiac remodelling in pre-eclampsia were demonstrated paving the way for larger studies.
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Affiliation(s)
- Kathleen Colford
- Centre for Medical Engineering, King's College London, London, UK,Centre for the Developing Brain, King's College London, London, UK
| | - Anthony N. Price
- Centre for Medical Engineering, King's College London, London, UK,Centre for the Developing Brain, King's College London, London, UK
| | - Julie Sigurdardottir
- Centre for Medical Engineering, King's College London, London, UK,Centre for the Developing Brain, King's College London, London, UK
| | - Anastasia Fotaki
- Department of Congenital Heart Disease, Evelina Children's Hospital, London, United Kingdom
| | - Johannes Steinweg
- Centre for Medical Engineering, King's College London, London, UK,Centre for the Developing Brain, King's College London, London, UK
| | - Lisa Story
- Academic Women's Health Department, King's College London, London, UK
| | - Alison Ho
- Academic Women's Health Department, King's College London, London, UK
| | - Lucy C. Chappell
- Academic Women's Health Department, King's College London, London, UK
| | - Joseph V. Hajnal
- Centre for Medical Engineering, King's College London, London, UK,Centre for the Developing Brain, King's College London, London, UK
| | - Mary Rutherford
- Centre for Medical Engineering, King's College London, London, UK,Centre for the Developing Brain, King's College London, London, UK
| | - Kuberan Pushparajah
- Centre for Medical Engineering, King's College London, London, UK,Department of Congenital Heart Disease, Evelina Children's Hospital, London, United Kingdom
| | - Pablo Lamata
- Centre for Medical Engineering, King's College London, London, UK
| | - Jana Hutter
- Centre for Medical Engineering, King's College London, London, UK,Centre for the Developing Brain, King's College London, London, UK,Corresponding author. Perinatal Imaging, 1st Floor South Wing, St THomas' Hospital, Westminster Bridge Road, SE17EH, London, UK.
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33
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Gale-Grant O, Fenn-Moltu S, França LGS, Dimitrova R, Christiaens D, Cordero-Grande L, Chew A, Falconer S, Harper N, Price AN, Hutter J, Hughes E, O'Muircheartaigh J, Rutherford M, Counsell SJ, Rueckert D, Nosarti C, Hajnal JV, McAlonan G, Arichi T, Edwards AD, Batalle D. Effects of gestational age at birth on perinatal structural brain development in healthy term-born babies. Hum Brain Mapp 2022; 43:1577-1589. [PMID: 34897872 PMCID: PMC8886657 DOI: 10.1002/hbm.25743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 11/19/2021] [Accepted: 11/30/2021] [Indexed: 11/12/2022] Open
Abstract
Infants born in early term (37-38 weeks gestation) experience slower neurodevelopment than those born at full term (40-41 weeks gestation). While this could be due to higher perinatal morbidity, gestational age at birth may also have a direct effect on the brain. Here we characterise brain volume and white matter correlates of gestational age at birth in healthy term-born neonates and their relationship to later neurodevelopmental outcome using T2 and diffusion weighted MRI acquired in the neonatal period from a cohort (n = 454) of healthy babies born at term age (>37 weeks gestation) and scanned between 1 and 41 days after birth. Images were analysed using tensor-based morphometry and tract-based spatial statistics. Neurodevelopment was assessed at age 18 months using the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III). Infants born earlier had higher relative ventricular volume and lower relative brain volume in the deep grey matter, cerebellum and brainstem. Earlier birth was also associated with lower fractional anisotropy, higher mean, axial, and radial diffusivity in major white matter tracts. Gestational age at birth was positively associated with all Bayley-III subscales at age 18 months. Regression models predicting outcome from gestational age at birth were significantly improved after adding neuroimaging features associated with gestational age at birth. This work adds to the body of evidence of the impact of early term birth and highlights the importance of considering the effect of gestational age at birth in future neuroimaging studies including term-born babies.
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Affiliation(s)
- Oliver Gale-Grant
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Sunniva Fenn-Moltu
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Lucas G S França
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Ralica Dimitrova
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Daan Christiaens
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Andrew Chew
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Shona Falconer
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Nicholas Harper
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Anthony N Price
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Emer Hughes
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Jonathan O'Muircheartaigh
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Mary Rutherford
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, UK.,Department of Medicine and Informatics, Technical University of Munich, Munich, Germany
| | - Chiara Nosarti
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Grainne McAlonan
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,Paediatric Neurosciences, Evelina London Children's Hospital Guy's and St Thomas' NHS Foundation Trust, London, UK.,Department of Bioengineering, Imperial College London, London, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Dafnis Batalle
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
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34
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Fenchel D, Dimitrova R, Robinson EC, Batalle D, Chew A, Falconer S, Kyriakopoulou V, Nosarti C, Hutter J, Christiaens D, Pietsch M, Brandon J, Hughes EJ, Allsop J, O'Keeffe C, Price AN, Cordero-Grande L, Schuh A, Makropoulos A, Passerat-Palmbach J, Bozek J, Rueckert D, Hajnal JV, McAlonan G, Edwards AD, O'Muircheartaigh J. Neonatal multi-modal cortical profiles predict 18-month developmental outcomes. Dev Cogn Neurosci 2022; 54:101103. [PMID: 35364447 PMCID: PMC8971851 DOI: 10.1016/j.dcn.2022.101103] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 02/08/2022] [Accepted: 03/23/2022] [Indexed: 12/16/2022] Open
Abstract
Developmental delays in infanthood often persist, turning into life-long difficulties, and coming at great cost for the individual and community. By examining the developing brain and its relation to developmental outcomes we can start to elucidate how the emergence of brain circuits is manifested in variability of infant motor, cognitive and behavioural capacities. In this study, we examined if cortical structural covariance at birth, indexing coordinated development, is related to later infant behaviour. We included 193 healthy term-born infants from the Developing Human Connectome Project (dHCP). An individual cortical connectivity matrix derived from morphological and microstructural features was computed for each subject (morphometric similarity networks, MSNs) and was used as input for the prediction of behavioural scores at 18 months using Connectome-Based Predictive Modeling (CPM). Neonatal MSNs successfully predicted social-emotional performance. Predictive edges were distributed between and within known functional cortical divisions with a specific important role for primary and posterior cortical regions. These results reveal that multi-modal neonatal cortical profiles showing coordinated maturation are related to developmental outcomes and that network organization at birth provides an early infrastructure for future functional skills.
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Affiliation(s)
- Daphna Fenchel
- MRC Centre for Neurodevelopmental Disorders, King's College London, London SE1 1UL, UK; Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK
| | - Ralica Dimitrova
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Emma C Robinson
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, UK
| | - Dafnis Batalle
- Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK; Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Andrew Chew
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Shona Falconer
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Vanessa Kyriakopoulou
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Chiara Nosarti
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK; Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK
| | - Jana Hutter
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Daan Christiaens
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Maximilian Pietsch
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Jakki Brandon
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Emer J Hughes
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Joanna Allsop
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Camilla O'Keeffe
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Anthony N Price
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK
| | - Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK
| | | | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK; Institute für Artificial Intelligence and Informatics in Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Joseph V Hajnal
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Grainne McAlonan
- MRC Centre for Neurodevelopmental Disorders, King's College London, London SE1 1UL, UK; Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK; South London and Maudsley NHS Foundation Trust, London SE5 8AZ, UK
| | - A David Edwards
- MRC Centre for Neurodevelopmental Disorders, King's College London, London SE1 1UL, UK; Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Jonathan O'Muircheartaigh
- MRC Centre for Neurodevelopmental Disorders, King's College London, London SE1 1UL, UK; Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK; Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK.
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35
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Ho A, Chappell LC, Story L, Al-Adnani M, Egloff A, Routledge E, Rutherford M, Hutter J. Corrigendum to "Visual assessment of the placenta in antenatal magnetic resonance imaging across gestation in normal and compromised pregnancies: Observations from a large cohort study" [117 January 2022 29-38]. Placenta 2022; 119:31. [PMID: 35078025 DOI: 10.1016/j.placenta.2022.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Alison Ho
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, United Kingdom.
| | - Lucy C Chappell
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, United Kingdom
| | - Lisa Story
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, United Kingdom
| | - Mudher Al-Adnani
- Department of Cellular Pathology, Guy's and St Thomas' Hospital, London, United Kingdom
| | - Alexia Egloff
- Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Emma Routledge
- Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Mary Rutherford
- Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, King's College London, London, United Kingdom; Biomedical Engineering Department, King's College London, London, United Kingdom
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36
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Ho A, Chappell LC, Story L, Al-Adnani M, Egloff A, Routledge E, Rutherford M, Hutter J. Visual assessment of the placenta in antenatal magnetic resonance imaging across gestation in normal and compromised pregnancies: Observations from a large cohort study. Placenta 2022; 117:29-38. [PMID: 34768166 PMCID: PMC8761363 DOI: 10.1016/j.placenta.2021.10.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 09/12/2021] [Accepted: 10/06/2021] [Indexed: 01/03/2023]
Abstract
INTRODUCTION Visual assessment of the placenta in antenatal magnetic resonance imaging is important to confirm healthy appearances or to identify pathology complicating fetal anomaly or maternal disease. METHODS We assessed the placenta in a large cohort of 228 women with low and high risk pregnancies across gestation. All women gave written informed consent and were imaged using either a 3T Philips Achieva or 1.5T Philips Ingenia scanner. Images were acquired with a T2-weighted single shot turbo spin echo sequence of the whole uterus (thereby including placenta) for anatomical information. RESULTS A structured approach to visual assessment of the placenta on T2-weighted imaging has been provided including determination of key anatomical landmarks to aid orientation, placental shape, signal intensity, lobularity and granularity. Transient factors affecting imaging are shown including the effect of fetal movement, gross fetal motion and contractions. Placental appearances across gestation in low risk pregnancies are shown and compared to pregnancies complicated by preeclampsia and chronic hypertension. The utility of other magnetic resonance techniques (T2* mapping as an indirect marker for quantifying oxygenation) and histological assessment alongside visual assessment of placental T2-weighted imaging are demonstrated. DISCUSSION A systematic approach with qualitative descriptors for placental visual assessment using T2-weighted imaging allows confirmation of normal placental development and can detect placental abnormalities in pregnancy complications. T2-weighted imaging can be visually assessed alongside functional imaging (such as T2* maps) in order to further probe the visual characteristics seen.
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Affiliation(s)
- Alison Ho
- Department of Women and Children’s Health, School of Life Course Sciences, King’s College London, London, United Kingdom
| | - Lucy C. Chappell
- Department of Women and Children’s Health, School of Life Course Sciences, King’s College London, London, United Kingdom
| | - Lisa Story
- Department of Women and Children’s Health, School of Life Course Sciences, King’s College London, London, United Kingdom
| | - Mudher Al-Adnani
- Department of Cellular Pathology, Guy’s and St Thomas’ Hospital, London, United Kingdom
| | - Alexia Egloff
- Centre for the Developing Brain, King’s College London, London, United Kingdom
| | - Emma Routledge
- Centre for the Developing Brain, King’s College London, London, United Kingdom
| | - Mary Rutherford
- Centre for the Developing Brain, King’s College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, King’s College London, London, United Kingdom,Biomedical Engineering Department, King’s College London, London, United Kingdom
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37
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Slator PJ, Palombo M, Miller KL, Westin C, Laun F, Kim D, Haldar JP, Benjamini D, Lemberskiy G, de Almeida Martins JP, Hutter J. Combined diffusion-relaxometry microstructure imaging: Current status and future prospects. Magn Reson Med 2021; 86:2987-3011. [PMID: 34411331 PMCID: PMC8568657 DOI: 10.1002/mrm.28963] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 06/25/2021] [Accepted: 07/20/2021] [Indexed: 12/15/2022]
Abstract
Microstructure imaging seeks to noninvasively measure and map microscopic tissue features by pairing mathematical modeling with tailored MRI protocols. This article reviews an emerging paradigm that has the potential to provide a more detailed assessment of tissue microstructure-combined diffusion-relaxometry imaging. Combined diffusion-relaxometry acquisitions vary multiple MR contrast encodings-such as b-value, gradient direction, inversion time, and echo time-in a multidimensional acquisition space. When paired with suitable analysis techniques, this enables quantification of correlations and coupling between multiple MR parameters-such as diffusivity, T 1 , T 2 , and T 2 ∗ . This opens the possibility of disentangling multiple tissue compartments (within voxels) that are indistinguishable with single-contrast scans, enabling a new generation of microstructural maps with improved biological sensitivity and specificity.
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Affiliation(s)
- Paddy J. Slator
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Marco Palombo
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Carl‐Fredrik Westin
- Department of RadiologyBrigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA
| | - Frederik Laun
- Institute of RadiologyUniversity Hospital ErlangenFriedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Daeun Kim
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
- Signal and Image Processing InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Justin P. Haldar
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
- Signal and Image Processing InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Dan Benjamini
- The Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBethesdaMDUSA
- The Center for Neuroscience and Regenerative MedicineUniformed Service University of the Health SciencesBethesdaMDUSA
| | | | - Joao P. de Almeida Martins
- Division of Physical Chemistry, Department of ChemistryLund UniversityLundSweden
- Department of Radiology and Nuclear MedicineSt. Olav’s University HospitalTrondheimNorway
| | - Jana Hutter
- Centre for Biomedical EngineeringSchool of Biomedical Engineering and ImagingKing’s College LondonLondonUK
- Centre for the Developing BrainSchool of Biomedical Engineering and ImagingKing’s College LondonLondonUK
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38
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Dimitrova R, Pietsch M, Ciarrusta J, Fitzgibbon SP, Williams LZJ, Christiaens D, Cordero-Grande L, Batalle D, Makropoulos A, Schuh A, Price AN, Hutter J, Teixeira RP, Hughes E, Chew A, Falconer S, Carney O, Egloff A, Tournier JD, McAlonan G, Rutherford MA, Counsell SJ, Robinson EC, Hajnal JV, Rueckert D, Edwards AD, O'Muircheartaigh J. Preterm birth alters the development of cortical microstructure and morphology at term-equivalent age. Neuroimage 2021; 243:118488. [PMID: 34419595 PMCID: PMC8526870 DOI: 10.1016/j.neuroimage.2021.118488] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/16/2021] [Accepted: 08/19/2021] [Indexed: 11/27/2022] Open
Abstract
INTRODUCTION The dynamic nature and complexity of the cellular events that take place during the last trimester of pregnancy make the developing cortex particularly vulnerable to perturbations. Abrupt interruption to normal gestation can lead to significant deviations to many of these processes, resulting in atypical trajectory of cortical maturation in preterm birth survivors. METHODS We sought to first map typical cortical micro- and macrostructure development using invivo MRI in a large sample of healthy term-born infants scanned after birth (n = 259). Then we offer a comprehensive characterization of the cortical consequences of preterm birth in 76 preterm infants scanned at term-equivalent age (37-44 weeks postmenstrual age). We describe the group-average atypicality, the heterogeneity across individual preterm infants, and relate individual deviations from normative development to age at birth and neurodevelopment at 18 months. RESULTS In the term-born neonatal brain, we observed heterogeneous and regionally specific associations between age at scan and measures of cortical morphology and microstructure, including rapid surface expansion, greater cortical thickness, lower cortical anisotropy and higher neurite orientation dispersion. By term-equivalent age, preterm infants had on average increased cortical tissue water content and reduced neurite density index in the posterior parts of the cortex, and greater cortical thickness anteriorly compared to term-born infants. While individual preterm infants were more likely to show extreme deviations (over 3.1 standard deviations) from normative cortical maturation compared to term-born infants, these extreme deviations were highly variable and showed very little spatial overlap between individuals. Measures of regional cortical development were associated with age at birth, but not with neurodevelopment at 18 months. CONCLUSION We showed that preterm birth alters cortical micro- and macrostructural maturation near the time of full-term birth. Deviations from normative development were highly variable between individual preterm infants.
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Affiliation(s)
- Ralica Dimitrova
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Maximilian Pietsch
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Judit Ciarrusta
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Sean P Fitzgibbon
- Centre for Functional MRI of the Brain (FMRIB), Welcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Logan Z J Williams
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Belgium
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Rui Pag Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Andrew Chew
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Shona Falconer
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Olivia Carney
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Alexia Egloff
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Grainne McAlonan
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom; South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Emma C Robinson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Faculty of Informatics and Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom
| | - Jonathan O'Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom.
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Berkowitsch A, Hutter J, Zaltsberg S, Tomic M, Kahle P, Hain A, Kuniss M, Neumann T. Impact of comorbidities and ablation strategy on outcome after pulmonary vein isolation with cryo-balloon in patients with non- paroxysmal atrial fibrillation. Eur Heart J 2021. [DOI: 10.1093/eurheartj/ehab724.0360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background
Presence of several comorbidities in patients with atrial fibrillation is well known, but impact of them on outcome after pulmonary vein isolation with cryo-balloon is not enough investigated. First aim of the study was analysis of the impact of comorbidities on long term outcome after PVI with cryo-balloon new generation (CBA) and secondary goal was evaluation of the impact of additional posterior roof ablation (PRA) in these patients.
Methods
Patients with non-paroxysmal AF ablated with CBA in our institution since May 2012 and completed follow up >3 months were enrolled in the study. The history of AF, cardiac comorbidities (CAD, Non ischemic-cardiomyopathy, heart insufficiency, right ventricular dysfunction) diabetes mellitus, and renal failure were assessed at admission, all patients received echocardiographic examination and blood test.
After a single trans-septal access and PV angiography PVI was performed using a 28-mm CBA. Mapping of PV signals before, during, and after each cryo application was performed with a 3F lasso catheter. The procedural endpoint after PVI was defined as complete elimination of all fragmented signals at the PV antrum with verification of entrance and exit block. In some patients PRA was performed additionally to PVI at discretion of physician. The primary endpoint of this study was the first documented recurrence of atrial tachyarrhythmia (>30 sec.), hospitalization due to cardio-vascular cause, re-do procedure or re-administration of anti-arrhythmic drugs.
Results
Among 560 patients 78 (13.9%) had no comorbidity and 299 (53.4%) were lasted with >1 comorbidity. A total of 260 (46.4%) recurrences were obtained within median follow up of 28 (12–57) months. Female gender, long time from first diagnosis >12 months and cardiac comorbidity were revealed to be independent predictors for long term recurrences whereas additional PRA performed in 176 pts independently improved outcome (61.9% vs 49.7%).
Conclusion
Cardiac comorbidities increased probability of post ablation recurrences, but performing of additional posterior roof ablation improved outcome in our cohort. These results should be confirmed in multi-center randomized study
Funding Acknowledgement
Type of funding sources: None.
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Affiliation(s)
- A Berkowitsch
- Johann Wolfgang Goethe University, Frankfurt, Germany
| | - J Hutter
- Johann Wolfgang Goethe University, Frankfurt, Germany
| | - S Zaltsberg
- Johann Wolfgang Goethe University, Frankfurt, Germany
| | - M Tomic
- Johann Wolfgang Goethe University, Frankfurt, Germany
| | - P Kahle
- Johann Wolfgang Goethe University, Frankfurt, Germany
| | - A Hain
- Johann Wolfgang Goethe University, Frankfurt, Germany
| | - M Kuniss
- Johann Wolfgang Goethe University, Frankfurt, Germany
| | - T Neumann
- Johann Wolfgang Goethe University, Frankfurt, Germany
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Hutter J, Jackson L, Ho A, Avena Zampieri C, Hajnal JV, Al-Adnani M, Nanda S, Shennan AH, Tribe RM, Gibbons D, Rutherford MA, Story L. The use of functional placental magnetic resonance imaging for assessment of the placenta after prolonged preterm rupture of the membranes in vivo: A pilot study. Acta Obstet Gynecol Scand 2021; 100:2244-2252. [PMID: 34546571 DOI: 10.1111/aogs.14267] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 08/18/2021] [Accepted: 08/31/2021] [Indexed: 12/29/2022]
Abstract
INTRODUCTION Preterm prelabor rupture of membranes (PPROM) complicates 3% of pregnancies in the UK. Where delivery does not occur spontaneously, expectant management until 37 weeks of gestation is advocated, unless signs of maternal infection develop. However, clinical presentation of maternal infection can be a late sign and injurious fetal inflammatory responses may already have been activated. There is therefore a need for more sensitive markers to aid optimal timing of interventions. At present there is no non-invasive test in clinical practice to assess for infection in the fetal compartment and definitive diagnosis of chorioamnionitis is by histological assessment of the placenta after delivery. This study presents comprehensive functional placental magnetic resonance imaging (MRI) quantification, already used in other organ systems, to assess for infection/inflammation, in women with and without PPROM aiming to explore its use as a biomarker for inflammation within the feto-placental compartment in vivo. MATERIAL AND METHODS Placental MRI scans were performed in a cohort of 12 women (with one having two scans) with PPROM before 34 weeks of gestation (selected because of their high risk of infection), and in a control group of 87 women. Functional placental assessment was performed with magnetic resonance techniques sensitive to changes in the microstructure (diffusion) and tissue composition (relaxometry), with quantification performed both over the entire organ and in regions of interest between the basal and chorionic plate. Placental histology was analyzed after delivery where available. RESULTS Normative evolution of functional magnetic resonance biomarkers over gestation was studied. Cases of inflammation, as assessed by histological presence of chorioamnionitis, and umbilical cord vasculitis with or without funisitis, were associated with lower T2* (mean T2* at 30 weeks 50 ms compared with 58 ms in controls) and higher fractional anisotropy (mean at 30 weeks 0.55 compared with 0.45 in controls). These differences did not reach significance and there was substantial heterogeneity both in T2* and Apparent Diffusivitiy across the cohort. CONCLUSIONS This first exploration of functional placental assessment in a cohort of women with PPROM demonstrates that functional placental MRI can reveal a range of placental changes associated with inflammatory processes. It is a promising tool to gain information and in the future to identify inflammation in vivo, and could therefore assist in improving optimal timing for interventions designed to prevent fetal injury.
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Affiliation(s)
- Jana Hutter
- Centre for Medical Engineering, King's College London, London, UK
| | - Laurence Jackson
- Centre for Medical Engineering, King's College London, London, UK
| | - Alison Ho
- Centre for Medical Engineering, King's College London, London, UK.,Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Carla Avena Zampieri
- Centre for Medical Engineering, King's College London, London, UK.,Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Joseph V Hajnal
- Centre for Medical Engineering, King's College London, London, UK
| | | | - Surabhi Nanda
- Peter Gorer Department of Immunobiology, King's College London, London, UK
| | - Andrew H Shennan
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Rachel M Tribe
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Deena Gibbons
- Peter Gorer Department of Immunobiology, King's College London, London, UK
| | | | - Lisa Story
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK.,Fetal Medicine Unit, St Thomas' Hospital, London, UK
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Pietsch M, Ho A, Bardanzellu A, Zeidan AMA, Chappell LC, Hajnal JV, Rutherford M, Hutter J. APPLAUSE: Automatic Prediction of PLAcental health via U-net Segmentation and statistical Evaluation. Med Image Anal 2021; 72:102145. [PMID: 34229190 PMCID: PMC8350147 DOI: 10.1016/j.media.2021.102145] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 04/26/2021] [Accepted: 06/21/2021] [Indexed: 02/04/2023]
Abstract
PURPOSE Artificial-intelligence population-based automated quantification of placental maturation and health from a rapid functional Magnetic Resonance scan. The placenta plays a crucial role for any successful human pregnancy. Deviations from the normal dynamic maturation throughout gestation are closely linked to major pregnancy complications. Antenatal assessment in-vivo using T2* relaxometry has shown great promise to inform management and possible interventions but clinical translation is hampered by time consuming manual segmentation and analysis techniques based on comparison against normative curves over gestation. METHODS This study proposes a fully automatic pipeline to predict the biological age and health of the placenta based on a free-breathing rapid (sub-30 second) T2* scan in two steps: Automatic segmentation using a U-Net and a Gaussian process regression model to characterize placental maturation and health. These are trained and evaluated on 108 3T MRI placental data sets, the evaluation included 20 high-risk pregnancies diagnosed with pre-eclampsia and/or fetal growth restriction. An independent cohort imaged at 1.5 T is used to assess the generalization of the training and evaluation pipeline. RESULTS Across low- and high-risk groups, automatic segmentation performs worse than inter-rater performance (mean Dice coefficients of 0.58 and 0.68, respectively) but is sufficient for estimating placental mean T2* (0.986 Pearson Correlation Coefficient). The placental health prediction achieves an excellent ability to differentiate cases of placental insufficiency between 27 and 33 weeks. High abnormality scores correlate with low birth weight, premature birth and histopathological findings. Retrospective application on a different cohort imaged at 1.5 T illustrates the ability for direct clinical translation. CONCLUSION The presented automatic pipeline facilitates a fast, robust and reliable prediction of placental maturation. It yields human-interpretable and verifiable intermediate results and quantifies uncertainties on the cohort-level and for individual predictions. The proposed machine-learning pipeline runs in close to real-time and, deployed in clinical settings, has the potential to become a cornerstone of diagnosis and intervention of placental insufficiency. APPLAUSE generalizes to an independent cohort imaged at 1.5 T, demonstrating robustness to different operational and clinical environments.
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Affiliation(s)
- Maximilian Pietsch
- Centre for Medical Engineering, King's College London, London, UK; Centre for the Developing Brain, King's College London, London, UK.
| | - Alison Ho
- Department of Women and Children's Health, King's College London, London, UK
| | - Alessia Bardanzellu
- Centre for Medical Engineering, King's College London, London, UK; Centre for the Developing Brain, King's College London, London, UK
| | - Aya Mutaz Ahmad Zeidan
- Centre for Medical Engineering, King's College London, London, UK; Centre for the Developing Brain, King's College London, London, UK
| | - Lucy C Chappell
- Department of Women and Children's Health, King's College London, London, UK
| | - Joseph V Hajnal
- Centre for Medical Engineering, King's College London, London, UK; Centre for the Developing Brain, King's College London, London, UK
| | - Mary Rutherford
- Centre for Medical Engineering, King's College London, London, UK; Centre for the Developing Brain, King's College London, London, UK
| | - Jana Hutter
- Centre for Medical Engineering, King's College London, London, UK; Centre for the Developing Brain, King's College London, London, UK
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Hutter J, Ho A, Jackson LH, Slator PJ, Chappell LC, Hajnal JV, Rutherford MA. An efficient and combined placental T 1 -ADC acquisition in pregnancies with and without pre-eclampsia. Magn Reson Med 2021; 86:2684-2691. [PMID: 34268807 DOI: 10.1002/mrm.28809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 03/15/2021] [Accepted: 03/26/2021] [Indexed: 11/07/2022]
Abstract
PURPOSE To provide a new approach to jointly assess microstructural and molecular properties of the human placenta in vivo fast and efficiently and to present initial evidence in cohorts of healthy pregnancies and those affected by pre-eclampsia. METHODS Slice and diffusion preparation shuffling, built on the previously proposed ZEBRA method, is presented as a robust and fast way to obtain T 1 and apparent diffusivity coefficient (ADC) values. Joint modeling and evaluation is performed on a cohort of healthy and pre-eclamptic participants at 3T. RESULTS The datasets show the ability to obtain robust and fast T 1 -ADC measurements. Significant decay over gestation in T 1 (-11 ms/week, P < . 05 ) and a trend toward significance in ADC (-0.23 mm/ s 2 /week, P = .08) values can be observed in a control cohort. Values for the pre-eclamptic pregnancies show a negative trend for both ADC and T 1 . CONCLUSIONS The presented sequence allows the simultaneous acquisition of 2 of the most promising quantitative parameters to study placental insufficiency-identified individually as relevant in previous studies-in under 2 minutes. This allows dynamic assessment of physiological processes, reduced inconsistency in spatial comparisons due to reduced motion artefacts and opens novel avenues for analysis. Initial results in pre-eclamptic placentas, with depicted changes in both ADC and T 1 , illustrate its potential to identify cases of placental insufficiency. Future work will focus on expanding the field-of-view using multi-band acceleration techniques and the expansion to larger and more diverse patient groups.
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Affiliation(s)
- Jana Hutter
- Center for Medical Engineering, King's College London, London, UK
- Center for the Developing Brain, School of Biomedical Engineering and Imaging, King's College London, London, UK
| | - Alison Ho
- Academic Women's Health Department, King's College London, London, UK
| | - Laurence H Jackson
- Center for Medical Engineering, King's College London, London, UK
- Center for the Developing Brain, School of Biomedical Engineering and Imaging, King's College London, London, UK
| | - Paddy J Slator
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Lucy C Chappell
- Academic Women's Health Department, King's College London, London, UK
| | - Joseph V Hajnal
- Center for Medical Engineering, King's College London, London, UK
- Center for the Developing Brain, School of Biomedical Engineering and Imaging, King's College London, London, UK
| | - Mary A Rutherford
- Center for Medical Engineering, King's College London, London, UK
- Center for the Developing Brain, School of Biomedical Engineering and Imaging, King's College London, London, UK
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Dimitrova R, Arulkumaran S, Carney O, Chew A, Falconer S, Ciarrusta J, Wolfers T, Batalle D, Cordero-Grande L, Price AN, Teixeira RPAG, Hughes E, Egloff A, Hutter J, Makropoulos A, Robinson EC, Schuh A, Vecchiato K, Steinweg JK, Macleod R, Marquand AF, McAlonan G, Rutherford MA, Counsell SJ, Smith SM, Rueckert D, Hajnal JV, O’Muircheartaigh J, Edwards AD. Phenotyping the Preterm Brain: Characterizing Individual Deviations From Normative Volumetric Development in Two Large Infant Cohorts. Cereb Cortex 2021; 31:3665-3677. [PMID: 33822913 PMCID: PMC8258435 DOI: 10.1093/cercor/bhab039] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 01/15/2021] [Accepted: 02/05/2021] [Indexed: 12/20/2022] Open
Abstract
The diverse cerebral consequences of preterm birth create significant challenges for understanding pathogenesis or predicting later outcome. Instead of focusing on describing effects common to the group, comparing individual infants against robust normative data offers a powerful alternative to study brain maturation. Here we used Gaussian process regression to create normative curves characterizing brain volumetric development in 274 term-born infants, modeling for age at scan and sex. We then compared 89 preterm infants scanned at term-equivalent age with these normative charts, relating individual deviations from typical volumetric development to perinatal risk factors and later neurocognitive scores. To test generalizability, we used a second independent dataset comprising of 253 preterm infants scanned using different acquisition parameters and scanner. We describe rapid, nonuniform brain growth during the neonatal period. In both preterm cohorts, cerebral atypicalities were widespread, often multiple, and varied highly between individuals. Deviations from normative development were associated with respiratory support, nutrition, birth weight, and later neurocognition, demonstrating their clinical relevance. Group-level understanding of the preterm brain disguises a large degree of individual differences. We provide a method and normative dataset that offer a more precise characterization of the cerebral consequences of preterm birth by profiling the individual neonatal brain.
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Affiliation(s)
- Ralica Dimitrova
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Sophie Arulkumaran
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Olivia Carney
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Andrew Chew
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Shona Falconer
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Judit Ciarrusta
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Thomas Wolfers
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen 6525EN, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen 6525EN, the Netherlands
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Biomedical Image Technologies, ETSI Telecomunicacion, Universidad Politecnica de Madrid and CIBER-BBN, Madrid 28040, Spain
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Rui P A G Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Alexia Egloff
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK
| | - Emma C Robinson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Johannes K Steinweg
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Russell Macleod
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen 6525EN, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen 6525EN, the Netherlands
| | - Grainne McAlonan
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London SE1 1UL, UK
- South London and Maudsley NHS Foundation Trust, London SE5 8AZ, UK
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Stephen M Smith
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London SE1 1UL, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London SE1 1UL, UK
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Myers R, Hutter J, Matthew J, Zhang T, Uus A, Lloyd D, Egloff A, Deprez M, Nanda S, Rutherford M, Story L. Assessment of the fetal thymus gland: Comparing MRI-acquired thymus volumes with 2D ultrasound measurements. Eur J Obstet Gynecol Reprod Biol 2021; 264:1-7. [PMID: 34246829 DOI: 10.1016/j.ejogrb.2021.06.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 04/30/2021] [Accepted: 06/14/2021] [Indexed: 11/29/2022]
Abstract
OBJECTIVES The fetal thymus gland has been shown to involute in response to intrauterine infection, and therefore could be used as a non-invasive marker of fetal compartment infection. The objective of this study was to evaluate how accurately 2D ultrasound-derived measurements of the fetal thymus reflect the 3D volume of the gland derived from motion corrected MRI images. STUDY DESIGN A retrospective study was performed using paired ultrasound and MRI datasets from the iFIND project (http://www.ifindproject.com). To obtain 3D volumetry of the thymus gland, T2-weighted single shot turbo spin echo (ssTSE) sequences of the fetal thorax were acquired. Thymus volumes were manually segmented from deformable slice-to-volume reconstructed images. To obtain 2D ultrasound measurements, previously stored fetal cine loops were used and measurements obtained at the 3-vessel-view (3VV) and 3-vessel-trachea view (3VT): anterior-posterior diameter (APD), intrathoracic diameter (ITD), transverse diameter (TD), perimeter and 3-vessel-edge (3VE). Inter-observer and intra-observer reliability (ICC) was calculated for both MRI and ultrasound measurements. Pearson correlation coefficients (PCC) were used to compare 2D-parameters with acceptable ICC to TV. RESULTS 38 participants were identified. Adequate visualisation was possible on 37 MRI scans and 31 ultrasound scans. Of the 30 datasets where both MRI and ultrasound data were available, MRI had good interobserver reliability (ICC 0.964) and all ultrasound 3VV 2D-parameters and 3VT 3VE had acceptable ICC (>0.75). Four 2D parameters were reflective of the 3D thymus volume: 3VV TD r = 0.540 (P = 0.002); 3VV perimeter r = 0.446 (P = 0.013); 3VV APD r = 0.435 (P = 0.110) and 3VT TD r = 0.544 (P = 0.002). CONCLUSIONS MRI appeared superior to ultrasound for visualization of the thymus gland and reproducibility of measurements. Three 2D US parameters, 3VV TD, perimeter and 3VT APD, correlated well with TV. Therefore, these represent a more accurate reflection of the true size of the gland than other 2D measurements, where MRI is not available.
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Affiliation(s)
- Rebecca Myers
- King's College London School of Bioscience, St George's, University of London, UK
| | - Jana Hutter
- Department of Perinatal Imaging, School of Biomedical Engineering, King's College London, UK
| | - Jacqueline Matthew
- Department of Perinatal Imaging, School of Biomedical Engineering, King's College London, UK
| | - Tong Zhang
- Artificial Intelligence Research Center, Peng Cheng Laboratory, Shenzhen, China
| | - Alena Uus
- Department of Perinatal Imaging, School of Biomedical Engineering, King's College London, UK
| | - David Lloyd
- Department of Perinatal Imaging, School of Biomedical Engineering, King's College London, UK
| | - Alexia Egloff
- Department of Perinatal Imaging, School of Biomedical Engineering, King's College London, UK
| | - Maria Deprez
- Department of Perinatal Imaging, School of Biomedical Engineering, King's College London, UK
| | - Surabhi Nanda
- Department of Fetal Medicine, St Thomas' Hospital London, UK
| | - Mary Rutherford
- Department of Perinatal Imaging, School of Biomedical Engineering, King's College London, UK
| | - Lisa Story
- Department of Fetal Medicine, St Thomas' Hospital London, UK; Department of Women and Children's Health King's College London, UK.
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Uus A, Grigorescu I, Pietsch M, Batalle D, Christiaens D, Hughes E, Hutter J, Cordero Grande L, Price AN, Tournier JD, Rutherford MA, Counsell SJ, Hajnal JV, Edwards AD, Deprez M. Multi-Channel 4D Parametrized Atlas of Macro- and Microstructural Neonatal Brain Development. Front Neurosci 2021; 15:661704. [PMID: 34220423 PMCID: PMC8248811 DOI: 10.3389/fnins.2021.661704] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 05/20/2021] [Indexed: 11/19/2022] Open
Abstract
Structural (also known as anatomical) and diffusion MRI provide complimentary anatomical and microstructural characterization of early brain maturation. However, the existing models of the developing brain in time include only either structural or diffusion MRI channels. Furthermore, there is a lack of tools for combined analysis of structural and diffusion MRI in the same reference space. In this work, we propose a methodology to generate a multi-channel (MC) continuous spatio-temporal parametrized atlas of the brain development that combines multiple MRI-derived parameters in the same anatomical space during 37-44 weeks of postmenstrual age range. We co-align structural and diffusion MRI of 170 normal term subjects from the developing Human Connectomme Project using MC registration driven by both T2-weighted and orientation distribution functions channels and fit the Gompertz model to the signals and spatial transformations in time. The resulting atlas consists of 14 spatio-temporal microstructural indices and two parcellation maps delineating white matter tracts and neonatal transient structures. In order to demonstrate applicability of the atlas for quantitative region-specific studies, a comparison analysis of 140 term and 40 preterm subjects scanned at the term-equivalent age is performed using different MRI-derived microstructural indices in the atlas reference space for multiple white matter regions, including the transient compartments. The atlas and software will be available after publication of the article.
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Affiliation(s)
- Alena Uus
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Irina Grigorescu
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Maximilian Pietsch
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Dafnis Batalle
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Emer Hughes
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Lucilio Cordero Grande
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
- Biomedical Image Technologies, ETSI Telecomunicacion, Universidad Politécnica de Madrid, CIBER-BBN, Madrid, Spain
| | - Anthony N. Price
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Mary A. Rutherford
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Serena J. Counsell
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Joseph V. Hajnal
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - A. David Edwards
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Maria Deprez
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
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Story L, Zhang T, Uus A, Hutter J, Egloff A, Gibbons D, Ho A, Al-Adnani M, Knight CL, Theodoulou I, Deprez M, Seed PT, Tribe RM, Shennan AH, Rutherford M. Antenatal thymus volumes in fetuses that delivered <32 weeks' gestation: An MRI pilot study. Acta Obstet Gynecol Scand 2021; 100:1040-1050. [PMID: 32865812 PMCID: PMC7614117 DOI: 10.1111/aogs.13983] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 08/11/2020] [Accepted: 08/13/2020] [Indexed: 01/31/2023]
Abstract
INTRODUCTION Infection and inflammation have been implicated in the etiology and subsequent morbidity associated with preterm birth. At present, there are no tests to assess for fetal compartment infection. The thymus, a gland integral in the fetal immune system, has been shown to involute in animal models of antenatal infection, but its response in human fetuses has not been studied. This study aims: (a) to generate magnetic resonance imaging (MRI) -derived fetal thymus volumes standardized for fetal weight; (b) to compare standardized thymus volumes from fetuses that delivered before 32 weeks of gestation with fetuses that subsequently deliver at term; (c) to assess thymus size as a predictor of preterm birth; and (d) to correlate the presence of chorioamnionitis and funisitis at delivery with thymic volumes in utero in fetuses that subsequently deliver preterm. MATERIAL AND METHODS Women at high-risk of preterm birth at 20-32 weeks of gestation were recruited. A control group was obtained from existing data sets acquired as part of three research studies. A fetal MRI was performed on a 1.5T or 3T MRI scanner: T2 weighted images were obtained of the entire uterine content and specifically the fetal thorax. A slice-to-volume registration method was used for reconstruction of three-dimensional images of the thorax. Thymus segmentations were performed manually. Body volumes were calculated by manual segmentation and thymus:body volume ratios were generated. Comparison of groups was performed using multiple regression analysis. Normal ranges were created for thymus volume and thymus:body volume ratios using the control data. Receiver operating curves (ROC) curves were generated for thymus:body volume ratio and gestation-adjusted thymus volume centiles as predictors of preterm birth. Placental histology was analyzed where available from pregnancies that delivered very preterm and the presence of chorioamnionitis/funisitis was noted. RESULTS Normative ranges were created for thymus volume, and thymus volume was standardized for fetal size from fetuses that subsequently delivered at term, but were imaged at 20-32 weeks of gestation. Image data sets from 16 women that delivered <32 weeks of gestation (ten with ruptured membranes and six with intact membranes) and 80 control women that delivered >37 weeks were included. Mean gestation at MRI of the study group was 28+4 weeks (SD 3.2) and for the control group was 25+5 weeks (SD 2.4). Both absolute fetal thymus volumes and thymus:body volume ratios were smaller in fetuses that delivered preterm (P < .001). Of the 16 fetuses that delivered preterm, 13 had placental histology, 11 had chorioamnionitis, and 9 had funisitis. The strongest predictors of prematurity were the thymus volume Z-score and thymus:body volume ratio Z-score (ROC areas 0.915 and 0.870, respectively). CONCLUSIONS We have produced MRI-derived normal ranges for fetal thymus and thymus:body volume ratios between 20 and 32 weeks of gestation. Fetuses that deliver very preterm had reduced thymus volumes when standardized for fetal size. A reduced thymus volume was also a predictor of spontaneous preterm delivery. Thymus volume may be a suitable marker of the fetal inflammatory response, although further work is needed to assess this, increasing the sample size to correlate the extent of chorioamnionitis with thymus size.
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Affiliation(s)
- Lisa Story
- Department of Women and Children’s Health, School of Life Sciences, King’s College London, London, UK,Fetal Medicine Unit, St Thomas’ Hospital, London, UK
| | - Tong Zhang
- Artificial Intelligence Research Center, Peng Cheng Laboratory, Shenzhen, China
| | - Alena Uus
- Centre for the Developing Brain and Centre for Medical Engineering, King’s College London, London, UK
| | - Jana Hutter
- Centre for the Developing Brain and Centre for Medical Engineering, King’s College London, London, UK
| | - Alexia Egloff
- Centre for the Developing Brain and Centre for Medical Engineering, King’s College London, London, UK
| | - Deena Gibbons
- Department of Immunobiology, King’s College London, London, UK
| | - Alison Ho
- Department of Women and Children’s Health, School of Life Sciences, King’s College London, London, UK
| | | | - Caroline L. Knight
- Department of Women and Children’s Health, School of Life Sciences, King’s College London, London, UK,Fetal Medicine Unit, St Thomas’ Hospital, London, UK
| | | | - Maria Deprez
- Artificial Intelligence Research Center, Peng Cheng Laboratory, Shenzhen, China
| | - Paul T. Seed
- Department of Women and Children’s Health, School of Life Sciences, King’s College London, London, UK
| | - Rachel M. Tribe
- Department of Women and Children’s Health, School of Life Sciences, King’s College London, London, UK
| | - Andrew H. Shennan
- Department of Women and Children’s Health, School of Life Sciences, King’s College London, London, UK
| | - Mary Rutherford
- Centre for the Developing Brain and Centre for Medical Engineering, King’s College London, London, UK
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Slator PJ, Hutter J, Marinescu RV, Palombo M, Jackson LH, Ho A, Chappell LC, Rutherford M, Hajnal JV, Alexander DC. Data-Driven multi-Contrast spectral microstructure imaging with InSpect: INtegrated SPECTral component estimation and mapping. Med Image Anal 2021; 71:102045. [PMID: 33934005 PMCID: PMC8543043 DOI: 10.1016/j.media.2021.102045] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 02/08/2021] [Accepted: 03/16/2021] [Indexed: 11/19/2022]
Abstract
Unsupervised learning technique for spectroscopic analysis of quantitative MRI. Shares information across voxels to improve estimation of multi-dimensional or single-dimensional spectra. Spectral maps are dramatically improved compared to existing approaches. Can potentially identify and map tissue environments; in placental diffusion-relaxometry MRI we demonstrate that it identifies components that correspond to distinct tissue types.
We introduce and demonstrate an unsupervised machine learning technique for spectroscopic analysis of quantitative MRI experiments. Our algorithm supports estimation of one-dimensional spectra from single-contrast data, and multidimensional correlation spectra from simultaneous multi-contrast data. These spectrum-based approaches allow model-free investigation of tissue properties, but require regularised inversion of a Laplace transform or Fredholm integral, which is an ill-posed calculation. Here we present a method that addresses this limitation in a data-driven way. The algorithm simultaneously estimates a canonical basis of spectral components and voxelwise maps of their weightings, thereby pooling information across whole images to regularise the ill-posed problem. We show in simulations that our algorithm substantially outperforms current voxelwise spectral approaches. We demonstrate the method on multi-contrast diffusion-relaxometry placental MRI scans, revealing anatomically-relevant sub-structures, and identifying dysfunctional placentas. Our algorithm vastly reduces the data required to reliably estimate spectra, opening up the possibility of quantitative MRI spectroscopy in a wide range of new applications. Our InSpect code is available at github.com/paddyslator/inspect.
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Affiliation(s)
- Paddy J Slator
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK.
| | - Jana Hutter
- Centre for the Developing Brain, Kings College London, London, UK; Biomedical Engineering Department, Kings College London, London, UK
| | - Razvan V Marinescu
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
| | - Marco Palombo
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
| | - Laurence H Jackson
- Centre for the Developing Brain, Kings College London, London, UK; Biomedical Engineering Department, Kings College London, London, UK
| | - Alison Ho
- Women's Health Department, King's College London, London, UK
| | - Lucy C Chappell
- Women's Health Department, King's College London, London, UK
| | - Mary Rutherford
- Centre for the Developing Brain, Kings College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, Kings College London, London, UK; Biomedical Engineering Department, Kings College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
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Hughes EJ, Price AN, McCabe L, Hiscocks S, Waite L, Green E, Hutter J, Pegoretti K, Cordero-Grande L, Edwards AD, Hajnal JV, Rutherford MA. The effect of maternal position on venous return for pregnant women during MRI. NMR Biomed 2021; 34:e4475. [PMID: 33480110 DOI: 10.1002/nbm.4475] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 12/20/2020] [Indexed: 06/12/2023]
Abstract
Magnetic resonance imaging (MRI) in pregnancy is commonly undertaken in the left lateral tilt (LLT) position to prevent inferior vena cava (IVC) compression and supine hypotensive events, although this may be suboptimal for image quality. The supine position may also have an adverse effect on fetal well-being. The spinal venous plexus may provide an alternative pathway for venous return in the presence of IVC compression. This study assesses morphology and blood flow of the IVC and spinal venous plexus for pregnant women in LLT and supine positions to ascertain the effect of maternal position on venous return during MRI. Eighty-two pregnant women underwent phase contrast MRI (PC-MRI) of the IVC and spinal venous plexus in the supine position; 25 were also imaged in the LLT position. Differences in life monitoring, IVC, spinal venous plexus and total venous return between the two positions were assessed. A linear regression assessed the relationship between flow in the IVC and the spinal venous plexus in the supine position. Increasing gestational age and the right-sided position of the uterus on IVC and spinal venous plexus venous return were also evaluated. Hypotension symptoms were similar in supine (10%) and LLT (8%) positioning. Supine positioning decreased IVC height (p < 0.004) and flow (p = 0.045) but flow in the spinal venous plexus increased (p < 0.001) compared with the LLT position. Total venous return showed no difference (p = 0.989) between the two positions. Additional measurements of flow in the aorta also showed no significant difference between the two groups (p = 0.866). Reduced IVC flow in the supine position was associated with increasing gestational age (p = 0.004) and degree of right-sided uterine position (p = 0.004). Women in the left lateral decubitus position who then rotated supine had greater flow in the IVC (p = 0.008) and spinal venous plexus (p = 0.029) than those who started supine. For the majority of women, the spinal venous plexus acts as a complementary venous return system for pregnant women in the supine position, maintaining vascular homeostasis. Further study is needed to assess the effects on the health of the fetus.
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Affiliation(s)
- Emer J Hughes
- Perinatal Imaging and Health, Kings College London, London, UK
| | | | - Laura McCabe
- Perinatal Imaging and Health, Kings College London, London, UK
| | | | - Lara Waite
- Perinatal Imaging and Health, Kings College London, London, UK
| | - Elaine Green
- Perinatal Imaging and Health, Kings College London, London, UK
| | - Jana Hutter
- Biomedical Engineering, Kings College London, London, UK
| | - Kelly Pegoretti
- Perinatal Imaging and Health, Kings College London, London, UK
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Story L, Davidson A, Patkee P, Fleiss B, Kyriakopoulou V, Colford K, Sankaran S, Seed P, Jones A, Hutter J, Shennan A, Rutherford M. Brain volumetry in fetuses that deliver very preterm: An MRI pilot study. Neuroimage Clin 2021; 30:102650. [PMID: 33838546 PMCID: PMC8045030 DOI: 10.1016/j.nicl.2021.102650] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 03/10/2021] [Accepted: 03/26/2021] [Indexed: 11/17/2022]
Abstract
Fetuses that subsequently deliver very preterm have a reduction in cortical and extra cerebrospinal fluid volumes. If such alterations commence antenatally this suggests a role for earlier administration of neuroprotective agents.
Background Infants born preterm are at increased risk of neurological complications resulting in significant morbidity and mortality. The exact mechanism and the impact of antenatal factors has not been fully elucidated, although antenatal infection/inflammation has been implicated in both the aetiology of preterm birth and subsequent neurological sequelae. It is therefore hypothesized that processes driving preterm birth are affecting brain development in utero. This study aims to compare MRI derived regional brain volumes in fetuses that deliver < 32 weeks with fetuses that subsequently deliver at term. Methods Women at high risk of preterm birth, with gestation 19.4–32 weeks were recruited prospectively. A control group was obtained from existing study datasets. Fetal MRI was performed on a 1.5 T or 3 T MRI scanner: T2-weighted images were obtained of the fetal brain. 3D brain volumetric datsets were produced using slice to volume reconstruction and regional segmentations were produced using multi-atlas approaches for supratentorial brain tissue, lateral ventricles, cerebellum cerebral cortex and extra-cerebrospinal fluid (eCSF). Statistical comparison of control and high-risk for preterm delivery fetuses was performed by creating normal ranges for each parameter from the control datasets and then calculating gestation adjusted z scores. Groups were compared using t-tests. Results Fetal image datasets from 24 pregnancies with delivery < 32 weeks and 87 control pregnancies that delivered > 37 weeks were included. Median gestation at MRI of the preterm group was 26.8 weeks (range 19.4–31.4) and control group 26.2 weeks (range 21.7–31.9). No difference was found in supra-tentorial brain volume, ventricular volume or cerebellar volume but the eCSF and cerebral cortex volumes were smaller in fetuses that delivered preterm (p < 0.001 in both cases). Conclusion Fetuses that deliver preterm have a reduction in cortical and eCSF volumes. This is a novel finding and needs further investigation. If alterations in brain development are commencing antenatally in fetuses that subsequently deliver preterm, this may present a window for in utero therapy in the future.
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Affiliation(s)
- Lisa Story
- Department of Women and Children's Health, King's College London, UK.
| | - Alice Davidson
- Centre for the Developing Brain, King's College London, London, UK
| | - Prachi Patkee
- Centre for the Developing Brain, King's College London, London, UK
| | - Bobbi Fleiss
- Centre for the Developing Brain, King's College London, London, UK; School of Health and Biomedical Sciences, RMIT University, Bundoora 3083, VIC, Australia; Université de Paris, NeuroDiderot, Inserm, F-75019 Paris, France
| | | | - Kathleen Colford
- Centre for the Developing Brain, King's College London, London, UK; Centre for Medical Engineering, King's College London, London, UK
| | | | - Paul Seed
- Department of Women and Children's Health, King's College London, UK
| | - Alice Jones
- Centre for the Developing Brain, King's College London, London, UK; Queen Mary University Medical School, UK
| | - Jana Hutter
- Centre for the Developing Brain, King's College London, London, UK; School of Health and Biomedical Sciences, RMIT University, Bundoora 3083, VIC, Australia
| | - Andrew Shennan
- Department of Women and Children's Health, King's College London, UK
| | - Mary Rutherford
- Centre for the Developing Brain, King's College London, London, UK
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50
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Steinweg JK, Hui GTY, Pietsch M, Ho A, van Poppel MP, Lloyd D, Colford K, Simpson JM, Razavi R, Pushparajah K, Rutherford M, Hutter J. T2* placental MRI in pregnancies complicated with fetal congenital heart disease. Placenta 2021; 108:23-31. [PMID: 33798991 DOI: 10.1016/j.placenta.2021.02.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 02/05/2021] [Accepted: 02/25/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND Congenital heart disease (CHD) is one of the most important and common group of congenital malformations in humans. Concurrent development and close functional links between the fetal heart and placenta emphasise the importance of understanding placental function and its influence in pregnancy outcomes. The aim of this study was to evaluate placental oxygenation by relaxometry (T2*) to assess differences in placental phenotype and function in CHD. METHODS In this prospective cross-sectional observational study, 69 women with a fetus affected with CHD and 37 controls, whole placental T2* was acquired using a 1.5-Tesla MRI scanner. Gaussian Process Regression was used to assess differences in placental phenotype in CHD cohorts compared to our controls. RESULTS Placental T2* maps demonstrated significant differences in CHD compared to controls at equivalent gestational age. Mean T2* values over the entire placental volume were lowest compared to predicted normal in right sided obstructive lesions (RSOL) (Z-Score 2.30). This cohort also showed highest lacunarity indices (Z-score -1.7), as a marker of lobule size. Distribution patterns of T2* values over the entire placental volume were positively skewed in RSOL (Z-score -4.69) and suspected, not confirmed coarctation of the aorta (CoA-) (Z-score -3.83). Deviations were also reflected in positive kurtosis in RSOL (Z-score -3.47) and CoA- (Z-score -2.86). CONCLUSION Placental structure and function appear to deviate from normal development in pregnancies with fetal CHD. Specific patterns of altered placental function assessed by T2* deliver crucial complementary information to antenatal assessments in the presence of fetal CHD.
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Affiliation(s)
- Johannes K Steinweg
- Department of Cardiovascular Imaging, School of Biomedical Engineering & Imaging Science, King's College London, London, United Kingdom.
| | - Grace Tin Yan Hui
- Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Maximilian Pietsch
- Centre for the Developing Brain, King's College London, London, United Kingdom; Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Science, King's College London, London, United Kingdom
| | - Alison Ho
- Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Milou Pm van Poppel
- Department of Cardiovascular Imaging, School of Biomedical Engineering & Imaging Science, King's College London, London, United Kingdom
| | - David Lloyd
- Department of Cardiovascular Imaging, School of Biomedical Engineering & Imaging Science, King's College London, London, United Kingdom; Department of Congenital Heart Disease, Evelina Children's Hospital, London, United Kingdom
| | - Kathleen Colford
- Centre for the Developing Brain, King's College London, London, United Kingdom
| | - John M Simpson
- Department of Cardiovascular Imaging, School of Biomedical Engineering & Imaging Science, King's College London, London, United Kingdom; Department of Congenital Heart Disease, Evelina Children's Hospital, London, United Kingdom
| | - Reza Razavi
- Department of Cardiovascular Imaging, School of Biomedical Engineering & Imaging Science, King's College London, London, United Kingdom; Department of Congenital Heart Disease, Evelina Children's Hospital, London, United Kingdom
| | - Kuberan Pushparajah
- Department of Cardiovascular Imaging, School of Biomedical Engineering & Imaging Science, King's College London, London, United Kingdom; Department of Congenital Heart Disease, Evelina Children's Hospital, London, United Kingdom
| | - Mary Rutherford
- Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, King's College London, London, United Kingdom; Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Science, King's College London, London, United Kingdom
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