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Valabregue R, Girka F, Pron A, Rousseau F, Auzias G. Comprehensive analysis of synthetic learning applied to neonatal brain MRI segmentation. Hum Brain Mapp 2024; 45:e26674. [PMID: 38651625 PMCID: PMC11036377 DOI: 10.1002/hbm.26674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 03/09/2024] [Accepted: 03/18/2024] [Indexed: 04/25/2024] Open
Abstract
Brain segmentation from neonatal MRI images is a very challenging task due to large changes in the shape of cerebral structures and variations in signal intensities reflecting the gestational process. In this context, there is a clear need for segmentation techniques that are robust to variations in image contrast and to the spatial configuration of anatomical structures. In this work, we evaluate the potential of synthetic learning, a contrast-independent model trained using synthetic images generated from the ground truth labels of very few subjects. We base our experiments on the dataset released by the developmental Human Connectome Project, for which high-quality images are available for more than 700 babies aged between 26 and 45 weeks postconception. First, we confirm the impressive performance of a standard UNet trained on a few volumes, but also confirm that such models learn intensity-related features specific to the training domain. We then confirm the robustness of the synthetic learning approach to variations in image contrast. However, we observe a clear influence of the age of the baby on the predictions. We improve the performance of this model by enriching the synthetic training set with realistic motion artifacts and over-segmentation of the white matter. Based on extensive visual assessment, we argue that the better performance of the model trained on real T2w data may be due to systematic errors in the ground truth. We propose an original experiment allowing us to show that learning from real data will reproduce any systematic bias affecting the training set, while synthetic models can avoid this limitation. Overall, our experiments confirm that synthetic learning is an effective solution for segmenting neonatal brain MRI. Our adapted synthetic learning approach combines key features that will be instrumental for large multisite studies and clinical applications.
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Affiliation(s)
- R. Valabregue
- CENIR, Institut du Cerveau (ICM)—Paris Brain Institute, Inserm U 1127, CNRS UMR 7225, Sorbonne UniversitéParisFrance
| | - F. Girka
- CENIR, Institut du Cerveau (ICM)—Paris Brain Institute, Inserm U 1127, CNRS UMR 7225, Sorbonne UniversitéParisFrance
| | - A. Pron
- Aix‐Marseille Université, CNRS, Institut de Neurosciences de la Timone, UMR 7289MarseilleFrance
| | - F. Rousseau
- IMT Atlantique, LaTIM INSERM U1101BrestFrance
| | - G. Auzias
- Aix‐Marseille Université, CNRS, Institut de Neurosciences de la Timone, UMR 7289MarseilleFrance
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Zhang W, Zhang X, Li L, Liao L, Zhao F, Zhong T, Pei Y, Xu X, Yang C, Zhang H, Li G. A joint brain extraction and image quality assessment framework for fetal brain MRI slices. Neuroimage 2024; 290:120560. [PMID: 38431181 DOI: 10.1016/j.neuroimage.2024.120560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024] Open
Abstract
Brain extraction and image quality assessment are two fundamental steps in fetal brain magnetic resonance imaging (MRI) 3D reconstruction and quantification. However, the randomness of fetal position and orientation, the variability of fetal brain morphology, maternal organs around the fetus, and the scarcity of data samples, all add excessive noise and impose a great challenge to automated brain extraction and quality assessment of fetal MRI slices. Conventionally, brain extraction and quality assessment are typically performed independently. However, both of them focus on the brain image representation, so they can be jointly optimized to ensure the network learns more effective features and avoid overfitting. To this end, we propose a novel two-stage dual-task deep learning framework with a brain localization stage and a dual-task stage for joint brain extraction and quality assessment of fetal MRI slices. Specifically, the dual-task module compactly contains a feature extraction module, a quality assessment head and a segmentation head with feature fusion for simultaneous brain extraction and quality assessment. Besides, a transformer architecture is introduced into the feature extraction module and the segmentation head. We utilize a multi-step training strategy to guarantee a stable and successful training of all modules. Finally, we validate our method by a 5-fold cross-validation and ablation study on a dataset with fetal brain MRI slices in different qualities, and perform a cross-dataset validation in addition. Experiments show that the proposed framework achieves very promising performance.
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Affiliation(s)
- Wenhao Zhang
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Xin Zhang
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China.
| | - Lingyi Li
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Lufan Liao
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Fenqiang Zhao
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA
| | - Tao Zhong
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA
| | - Yuchen Pei
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA
| | - Xiangmin Xu
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Chaoxiang Yang
- Department of Radiology, Guangdong Women and Children Hospital, Guangzhou, China
| | - He Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.
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3
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Urru A, Nakaki A, Benkarim O, Crovetto F, Segalés L, Comte V, Hahner N, Eixarch E, Gratacos E, Crispi F, Piella G, González Ballester MA. An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 230:107334. [PMID: 36682108 DOI: 10.1016/j.cmpb.2023.107334] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 11/29/2022] [Accepted: 01/02/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE The automatic segmentation of perinatal brain structures in magnetic resonance imaging (MRI) is of utmost importance for the study of brain growth and related complications. While different methods exist for adult and pediatric MRI data, there is a lack for automatic tools for the analysis of perinatal imaging. METHODS In this work, a new pipeline for fetal and neonatal segmentation has been developed. We also report the creation of two new fetal atlases, and their use within the pipeline for atlas-based segmentation, based on novel registration methods. The pipeline is also able to extract cortical and pial surfaces and compute features, such as curvature, local gyrification index, sulcal depth, and thickness. RESULTS Results show that the introduction of the new templates together with our segmentation strategy leads to accurate results when compared to expert annotations, as well as better performances when compared to a reference pipeline (developing Human Connectome Project (dHCP)), for both early and late-onset fetal brains. CONCLUSIONS These findings show the potential of the presented atlases and the whole pipeline for application in both fetal, neonatal, and longitudinal studies, which could lead to dramatic improvements in the understanding of perinatal brain development.
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Affiliation(s)
- Andrea Urru
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Ayako Nakaki
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain; Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Oualid Benkarim
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Francesca Crovetto
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain
| | - Laura Segalés
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain
| | - Valentin Comte
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Nadine Hahner
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain; Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Elisenda Eixarch
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain; Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Centre for Biomedical Research on Rare Diseases (CIBERER), Barcelona, Spain
| | - Eduard Gratacos
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain; Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Centre for Biomedical Research on Rare Diseases (CIBERER), Barcelona, Spain
| | - Fàtima Crispi
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain; Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Centre for Biomedical Research on Rare Diseases (CIBERER), Barcelona, Spain
| | - Gemma Piella
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Miguel A González Ballester
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; ICREA, Barcelona, Spain.
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Alenyá M, Wang X, Lefévre J, Auzias G, Fouquet B, Eixarch E, Rousseau F, Camara O. Computational pipeline for the generation and validation of patient-specific mechanical models of brain development. BRAIN MULTIPHYSICS 2022. [DOI: 10.1016/j.brain.2022.100045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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5
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Torres HR, Morais P, Oliveira B, Birdir C, Rüdiger M, Fonseca JC, Vilaça JL. A review of image processing methods for fetal head and brain analysis in ultrasound images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 215:106629. [PMID: 35065326 DOI: 10.1016/j.cmpb.2022.106629] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 12/20/2021] [Accepted: 01/08/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Examination of head shape and brain during the fetal period is paramount to evaluate head growth, predict neurodevelopment, and to diagnose fetal abnormalities. Prenatal ultrasound is the most used imaging modality to perform this evaluation. However, manual interpretation of these images is challenging and thus, image processing methods to aid this task have been proposed in the literature. This article aims to present a review of these state-of-the-art methods. METHODS In this work, it is intended to analyze and categorize the different image processing methods to evaluate fetal head and brain in ultrasound imaging. For that, a total of 109 articles published since 2010 were analyzed. Different applications are covered in this review, namely analysis of head shape and inner structures of the brain, standard clinical planes identification, fetal development analysis, and methods for image processing enhancement. RESULTS For each application, the reviewed techniques are categorized according to their theoretical approach, and the more suitable image processing methods to accurately analyze the head and brain are identified. Furthermore, future research needs are discussed. Finally, topics whose research is lacking in the literature are outlined, along with new fields of applications. CONCLUSIONS A multitude of image processing methods has been proposed for fetal head and brain analysis. Summarily, techniques from different categories showed their potential to improve clinical practice. Nevertheless, further research must be conducted to potentiate the current methods, especially for 3D imaging analysis and acquisition and for abnormality detection.
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Affiliation(s)
- Helena R Torres
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal; 2Ai - School of Technology, IPCA, Barcelos, Portugal.
| | - Pedro Morais
- 2Ai - School of Technology, IPCA, Barcelos, Portugal
| | - Bruno Oliveira
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal; 2Ai - School of Technology, IPCA, Barcelos, Portugal
| | - Cahit Birdir
- Department of Gynecology and Obstetrics, University Hospital Carl Gustav Carus, TU Dresden, Germany; Saxony Center for Feto-Neonatal Health, TU Dresden, Germany
| | - Mario Rüdiger
- Department for Neonatology and Pediatric Intensive Care, University Hospital Carl Gustav Carus, TU Dresden, Germany
| | - Jaime C Fonseca
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal
| | - João L Vilaça
- 2Ai - School of Technology, IPCA, Barcelos, Portugal
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Li X, Zhang S, Jiang X, Zhang S, Han J, Guo L, Zhang T. Cortical development coupling between surface area and sulcal depth on macaque brains. Brain Struct Funct 2022; 227:1013-1029. [PMID: 34989870 DOI: 10.1007/s00429-021-02444-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 12/15/2021] [Indexed: 02/06/2023]
Abstract
Postnatal development of cerebral cortex is associated with a variety of neuronal processes and is thus critical to development of brain function and cognition. Longitudinal changes of cortical morphology and topology, such as postnatal cortical thinning and flattening have been widely studied. However, thorough and systematic investigation of such cortical change, including how to quantify it from multiple spatial directions and how to relate it to surface topology, is rarely found. In this work, based on a longitudinal macaque neuroimaging dataset, we quantified local changes in gyral white matter's surface area and sulcal depth during early development. We also investigated how these two metrics are coupled and how this coupling is linked to cortical surface topology, underlying white matter, and positions of functional areas. Semi-parametric generalized additive models were adopted to quantify the longitudinal changes of surface area (A) and sulcal depth (D), and the coupling patterns between them. This resulted in four classes of regions, according to how they change compared with global change throughout early development: slower surface area change and slower sulcal depth change (slowA_slowD), slower surface area change and faster sulcal depth change (slowA_fastD), faster surface area change and slower sulcal depth change (fastA_slowD), and faster surface area change and faster sulcal depth change (fastA_fastD). We found that cortex-related metrics, including folding pattern and cortical thickness, vary along slowA_fastD-fastA_slowD axis, and structural connection-related metrics vary along fastA_fastD-slowA_slowD axis, with which brain functional sites align better. It is also found that cortical landmarks, including sulcal pits and gyral hinges, spatially reside on the borders of the four patterns. These findings shed new lights on the relationship between cortex development, surface topology, axonal wiring pattern and brain functions.
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Affiliation(s)
- Xiao Li
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Songyao Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Xi Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Shu Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China.
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7
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Rutherford S, Sturmfels P, Angstadt M, Hect J, Wiens J, van den Heuvel MI, Scheinost D, Sripada C, Thomason M. Automated Brain Masking of Fetal Functional MRI with Open Data. Neuroinformatics 2022; 20:173-185. [PMID: 34129169 PMCID: PMC9437772 DOI: 10.1007/s12021-021-09528-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/18/2021] [Indexed: 01/09/2023]
Abstract
Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses, we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open-source workflow, with openly shared code and data, for fetal functional MRI data preprocessing.
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Affiliation(s)
- Saige Rutherford
- Donders Institute, Radboud University Medical Center, Nijmegen, The Netherlands.
- Department of Psychiatry, University of Michigan, MI, Ann Arbor, USA.
| | - Pascal Sturmfels
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Mike Angstadt
- Department of Psychiatry, University of Michigan, MI, Ann Arbor, USA
| | - Jasmine Hect
- Department of Psychology, Wayne State University, Detroit, MI, USA
| | - Jenna Wiens
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | | | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Chandra Sripada
- Department of Psychiatry, University of Michigan, MI, Ann Arbor, USA
| | - Moriah Thomason
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA
- Department of Population Health, New York University School of Medicine, New York, NY, USA
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Wu J, Yu B, Wang L, Yang Q, Zhang Y. Longitudinal Chinese Population Structural Fetal Brain Atlases Construction: toward precise fetal brain segmentation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2745-2749. [PMID: 34891818 DOI: 10.1109/embc46164.2021.9630514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In magnetic resonance imaging (MRI) studies of fetal brain development, structural brain atlases usually serve as essential references for the fetal population. Individual images are spatially normalized into a common or standard atlas space to extract regional information on volumetric or morphological brain variations. However, the existing fetal brain atlases are mostly based on MR images obtained from Caucasian populations and thus are not ideal for the characterization of the brains of the Chinese population due to neuroanatomical differences related to genetic factors. In this paper, we use an unbiased template construction algorithm to create a set of age-specific Chinese fetal atlases between 21-35 weeks of gestation from 115 normally developing fetal brains. Based on the 4D spatiotemporal atlas, the morphologically developmental patterns, e.g., cortical thickness, sulcal and gyral patterns, were quantified from in utero MRI. Additionally, after applying the Chinese fetal atlases and Caucasian fetal atlases to an independent Chinese pediatric dataset, we find that the Chinese fetal atlases result in significantly higher accuracy than the Caucasian fetal atlases in guiding brain tissue segmentation. These results suggest that the Chinese fetal brain atlases are necessary for quantitative analysis of the typical and atypical development of the Chinese fetal population in the future.
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Pei Y, Chen L, Zhao F, Wu Z, Zhong T, Wang Y, Chen C, Wang L, Zhang H, Wang L, Li G. Learning Spatiotemporal Probabilistic Atlas of Fetal Brains with Anatomically Constrained Registration Network. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2021; 12907:239-248. [PMID: 35128549 PMCID: PMC8816449 DOI: 10.1007/978-3-030-87234-2_23] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Brain atlases are of fundamental importance for analyzing the dynamic neurodevelopment in fetal brain studies. Since the brain size, shape, and anatomical structures change rapidly during the prenatal period, it is essential to construct a spatiotemporal (4D) atlas equipped with tissue probability maps, which can preserve sharper early brain folding patterns for accurately characterizing dynamic changes in fetal brains and provide tissue prior informations for related tasks, e.g., segmentation, registration, and parcellation. In this work, we propose a novel unsupervised age-conditional learning framework to build temporally continuous fetal brain atlases by incorporating tissue segmentation maps, which outperforms previous traditional atlas construction methods in three aspects. First, our framework enables learning age-conditional deformable templates by leveraging the entire collection. Second, we leverage reliable brain tissue segmentation maps in addition to the low-contrast noisy intensity images to enhance the alignment of individual images. Third, a novel loss function is designed to enforce the similarity between the learned tissue probability map on the atlas and each subject tissue segmentation map after registration, thereby providing extra anatomical consistency supervision for atlas building. Our 4D temporally-continuous fetal brain atlases are constructed based on 82 healthy fetuses from 22 to 32 gestational weeks. Compared with the atlases built by the state-of-the-art algorithms, our atlases preserve more structural details and sharper folding patterns. Together with the learned tissue probability maps, our 4D fetal atlases provide a valuable reference for spatial normalization and analysis of fetal brain development.
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Affiliation(s)
- Yuchen Pei
- Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University, Shanghai, China
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Liangjun Chen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Fenqiang Zhao
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Tao Zhong
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Ya Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Changan Chen
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - He Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Lisheng Wang
- Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
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10
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Effect of MRI acquisition acceleration via compressed sensing and parallel imaging on brain volumetry. MAGNETIC RESONANCE MATERIALS IN PHYSICS, BIOLOGY AND MEDICINE 2021; 34:487-497. [PMID: 33502667 PMCID: PMC8338844 DOI: 10.1007/s10334-020-00906-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 12/15/2020] [Accepted: 12/30/2020] [Indexed: 11/06/2022]
Abstract
Objectives To investigate the effect of compressed SENSE (CS), an acceleration technique combining parallel imaging and compressed sensing, on potential bias and precision of brain volumetry and evaluate it in the context of normative brain volumetry. Materials and methods In total, 171 scans from scan-rescan experiments on three healthy subjects were analyzed. Each subject received 3D-T1-weighted brain MRI scans at increasing degrees of acceleration (CS-factor = 1/4/8/12/16/20/32). Single-scan acquisition times ranged from 00:41 min (CS-factor = 32) to 21:52 min (CS-factor = 1). Brain segmentation and volumetry was performed using two different software tools: md.brain, a proprietary software based on voxel-based morphometry, and FreeSurfer, an open-source software based on surface-based morphometry. Four sub-volumes were analyzed: brain parenchyma (BP), total gray matter, total white matter, and cerebrospinal fluid (CSF). Coefficient of variation (CoV) of the repeated measurements as a measure of intra-subject reliability was calculated. Intraclass correlation coefficient (ICC) with regard to increasing CS-factor was calculated as another measure of reliability. Noise-to-contrast ratio as a measure of image quality was calculated for each dataset to analyze the association between acceleration factor, noise and volumetric brain measurements. Results For all sub-volumes, there is a systematic bias proportional to the CS-factor which is dependent on the utilized software and subvolume. Measured volumes deviated significantly from the reference standard (CS-factor = 1), e.g. ranging from 1 to 13% for BP. The CS-induced systematic bias is driven by increased image noise. Except for CSF, reliability of brain volumetry remains high, demonstrated by low CoV (< 1% for CS-factor up to 20) and good to excellent ICC for CS-factor up to 12. Conclusion CS-acceleration has a systematic biasing effect on volumetric brain measurements. Supplementary Information The online version contains supplementary material available at 10.1007/s10334-020-00906-9.
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11
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Wu J, Sun T, Yu B, Li Z, Wu Q, Wang Y, Qian Z, Zhang Y, Jiang L, Wei H. Age-specific structural fetal brain atlases construction and cortical development quantification for chinese population. Neuroimage 2021; 241:118412. [PMID: 34298085 DOI: 10.1016/j.neuroimage.2021.118412] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 06/16/2021] [Accepted: 07/19/2021] [Indexed: 01/14/2023] Open
Abstract
In magnetic resonance imaging (MRI) studies of fetal brain development, structural brain atlases usually serve as essential references for the fetal population. Individual images are usually normalized into a common or standard space for analysis. However, the existing fetal brain atlases are mostly based on MR images obtained from Caucasian populations and thus are not ideal for the characterization of the fetal Chinese population due to neuroanatomical differences related to genetic factors. In this paper, we use an unbiased template construction algorithm to create a set of age-specific Chinese fetal atlases between 21-35 weeks of gestation from 115 normal fetal brains. Based on the 4D spatiotemporal atlas, the morphological development patterns, e.g., cortical thickness, cortical surface area, sulcal and gyral patterns, were quantified. The fetal brain abnormalities were detected when referencing the age-specific template. Additionally, a direct comparison of the Chinese fetal atlases and Caucasian fetal atlases reveals dramatic anatomical differences, mainly in the medial frontal and temporal regions. After applying the Chinese and Caucasian fetal atlases separately to an independent Chinese fetal brain dataset, we find that the Chinese fetal atlases result in significantly higher accuracy than the Caucasian fetal atlases in guiding brain tissue segmentation. These results suggest that the Chinese fetal brain atlases are necessary for quantitative analysis of the typical and atypical development of the Chinese fetal population in the future. The atlases with their parcellations are now publicly available at https://github.com/DeepBMI/FBA-Chinese.
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Affiliation(s)
- Jiangjie Wu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China; School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Taotao Sun
- Department of Radiology, International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China; Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China
| | - Boliang Yu
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Zhenghao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Qing Wu
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Yutong Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhaoxia Qian
- Department of Radiology, International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China; Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China
| | - Yuyao Zhang
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Ling Jiang
- Department of Radiology, International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China; Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China.
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China.
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12
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Ren JY, Zhu M, Dong SZ. Three-Dimensional Volumetric Magnetic Resonance Imaging Detects Early Alterations of the Brain Growth in Fetuses With Congenital Heart Disease. J Magn Reson Imaging 2021; 54:263-272. [PMID: 33559371 DOI: 10.1002/jmri.27526] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 12/29/2020] [Accepted: 12/30/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Several published studies have shown alterations of brain development in third-trimester fetuses with congenital heart disease (CHD). However, little is known about the timing and pattern of altered brain development in fetuses with CHD. PURPOSE To investigate the changes in the volume of intracranial structures in fetuses with CHD by three-dimensional (3D) volumetric magnetic resonance imaging (MRI) in the earlier stages of pregnancy (median gestational age [GA], 26 weeks). STUDY TYPE Retrospective. POPULATION Forty women carrying a fetus with CHD (including 20 fetuses with GA <26 weeks) and 120 pregnant women carrying a healthy fetus (including 50 fetuses with GA <26 weeks). FIELD STRENGTH/SEQUENCE Two-dimensional single-shot turbo spin echo sequence at 1.5 -T. ASSESSMENT Three-dimensional volumetric parameters from slice-to-volume registered images, including cortical gray matter volume (GMV), subcortical brain tissue volume (SBV), intracranial cavity volume (ICV), lateral ventricles volume (VV), cerebellum, brainstem, and extra-cerebrospinal fluid (e-CSF) were quantified by manual segmentation from one primary and two secondary observers. STATISTICAL TESTS Volumes were presented graphically with quadratic curve fitting. Scatterplots were produced mapping volumes against GA in normal and CHD fetuses. For GA <26 weeks, Z scores were calculated and Student's t-tests were conducted to compare volumes between the normal and CHD fetuses. RESULTS In fetuses with CHD GMV, SBV, cerebellum, and brainstem were significantly reduced (all P < 0.05) in early stages of pregnancy (GA <26 weeks), with differences becoming progressively greater with increasing GA. Compared with normal fetuses, e-CSF, e-CSF to ICV ratio, and VV were higher in fetuses with CHD (all P < 0.05). However, ICV volume and the GMV to SBV ratio were not significantly reduced in the CHD group (P = 0.94 and P = 0.13, respectively) during the middle gestation (GA <26 weeks). DATA CONCLUSION There appear to be alterations of brain development trajectory in CHD fetuses that can be detected by 3D volumetric MRI in the earlier stages of pregnancy. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Jing-Ya Ren
- Department of Radiology, Shanghai Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Ming Zhu
- Department of Radiology, Shanghai Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Su-Zhen Dong
- Department of Radiology, Shanghai Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai, China
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13
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A novel approach to multiple anatomical shape analysis: Application to fetal ventriculomegaly. Med Image Anal 2020; 64:101750. [PMID: 32559594 DOI: 10.1016/j.media.2020.101750] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 01/25/2020] [Accepted: 06/03/2020] [Indexed: 02/04/2023]
Abstract
Fetal ventriculomegaly (VM) is a condition in which one or both lateral ventricles are enlarged, and is diagnosed as an atrial diameter larger than 10 mm. Evidence of altered cortical folding associated with VM has been shown in the literature. However, existing works use a single scalar value such as diagnosis or lateral ventricular volume to characterize VM and study its relationship with alterations in cortical folding, thus failing to reveal the spatially-heterogeneous associations. In this work, we propose a novel approach to identify fine-grained associations between cortical folding and ventricular enlargement by leveraging the vertex-wise correlations between their growth patterns in terms of area expansion and curvature. Our approach comprises three steps. In the first step, we define a joint graph Laplacian matrix using cortex-to-ventricle correlations. The joint Laplacian is built based on multiple cortical features. Next, we propose a spectral embedding of the cortex-to-ventricle graph into a common underlying space where its nodes are projected according to the joint ventricle-cortex growth patterns. In this low-dimensional joint ventricle-cortex space, associated growth patterns lie close to each other. In the final step, we perform hierarchical clustering in the joint embedded space to identify associated sub-regions between cortex and ventricle. Using a dataset of 25 healthy fetuses and 23 fetuses with isolated non-severe VM within the age range of 26-29 gestational weeks, our approach reveals clinically relevant and heterogeneous regional associations. Cortical regions forming these associations are further validated using statistical analysis, revealing regions with altered folding that are significantly associated with ventricular dilation.
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14
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Joint Image Quality Assessment and Brain Extraction of Fetal MRI Using Deep Learning. MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION – MICCAI 2020 2020. [DOI: 10.1007/978-3-030-59725-2_40] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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15
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Xia J, Wang F, Wu Z, Wang L, Zhang C, Shen D, Li G. Mapping hemispheric asymmetries of the macaque cerebral cortex during early brain development. Hum Brain Mapp 2019; 41:95-106. [PMID: 31532054 PMCID: PMC7267900 DOI: 10.1002/hbm.24789] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 05/29/2019] [Accepted: 06/05/2019] [Indexed: 11/10/2022] Open
Abstract
Studying cortical hemispheric asymmetries during the dynamic early postnatal stages in macaque monkeys (with close phylogenetic relationship to humans) would increase our limited understanding on the possible origins, developmental trajectories, and evolutional mechanisms of brain asymmetries in nonhuman primates, but remains a blind spot to the community. Via cortical surface-based morphometry, we comprehensively analyze hemispheric structural asymmetries in 134 longitudinal MRI scans from birth to 20 months of age from 32 healthy macaque monkeys. We reveal that most clusters of hemispheric asymmetries of cortical properties, such as surface area, cortical thickness, sulcal depth, and vertex positions, expand in the first 4 months of life, and evolve only moderately thereafter. Prominent hemispheric asymmetries are found at the inferior frontal gyrus, precentral gyrus, posterior temporal cortex, superior temporal gyrus (STG), superior temporal sulcus (STS), and cingulate cortex. Specifically, the left planum temporale and left STG consistently have larger area and thicker cortices than those on the right hemisphere, while the right STS, right cingulate cortex, and right anterior insula are consistently deeper than the left ones, partially consistent with the findings in human infants and adults. Our results thus provide a valuable reference in studying early brain development and evolution.
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Affiliation(s)
- Jing Xia
- Department of Computer Science and Technology, Shandong University, Jinan, Shandong, China.,Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Fan Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Caiming Zhang
- Department of Computer Science and Technology, Shandong University, Jinan, Shandong, China
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
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16
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Xia J, Wang F, Benkarim OM, Sanroma G, Piella G, González Ballester MA, Hahner N, Eixarch E, Zhang C, Shen D, Li G. Fetal cortical surface atlas parcellation based on growth patterns. Hum Brain Mapp 2019; 40:3881-3899. [PMID: 31106942 DOI: 10.1002/hbm.24637] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 04/22/2019] [Accepted: 04/27/2019] [Indexed: 12/13/2022] Open
Abstract
Defining anatomically and functionally meaningful parcellation maps on cortical surface atlases is of great importance in surface-based neuroimaging analysis. The conventional cortical parcellation maps are typically defined based on anatomical cortical folding landmarks in adult surface atlases. However, they are not suitable for fetal brain studies, due to dramatic differences in brain size, shape, and properties between adults and fetuses. To address this issue, we propose a novel data-driven method for parcellation of fetal cortical surface atlases into distinct regions based on the dynamic "growth patterns" of cortical properties (e.g., surface area) from a population of fetuses. Our motivation is that the growth patterns of cortical properties indicate the underlying rapid changes of microstructures, which determine the molecular and functional principles of the cortex. Thus, growth patterns are well suitable for defining distinct cortical regions in development, structure, and function. To comprehensively capture the similarities of cortical growth patterns among vertices, we construct two complementary similarity matrices. One is directly based on the growth trajectories of vertices, and the other is based on the correlation profiles of vertices' growth trajectories in relation to a set of reference points. Then, we nonlinearly fuse these two similarity matrices into a single one, which can better capture both their common and complementary information than by simply averaging them. Finally, based on this fused similarity matrix, we perform spectral clustering to divide the fetal cortical surface atlases into distinct regions. By applying our method on 25 normal fetuses from 26 to 29 gestational weeks, we construct age-specific fetal cortical surface atlases equipped with biologically meaningful parcellation maps based on cortical growth patterns. Importantly, our generated parcellation maps reveal spatially contiguous, hierarchical and bilaterally relatively symmetric patterns of fetal cortical surface development.
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Affiliation(s)
- Jing Xia
- Department of Computer Science and Technology, Shandong University, Shandong, China.,Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hills, North Carolina
| | - Fan Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hills, North Carolina
| | | | - Gerard Sanroma
- BCN Medtech, Universitat Pompeu Fabra, Barcelona, Spain.,German Center for Neurodegenerative Diseases, Bonn, Germany
| | - Gemma Piella
- BCN Medtech, Universitat Pompeu Fabra, Barcelona, Spain
| | | | - Nadine Hahner
- Fetal i+D Fetal Medicine Research Center, BCNatal, Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Institut Clínic de Ginecologia, Obstetricia i Neonatologia, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Universitat de Barcelona, Barcelona, Spain
| | - Elisenda Eixarch
- Fetal i+D Fetal Medicine Research Center, BCNatal, Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Institut Clínic de Ginecologia, Obstetricia i Neonatologia, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Universitat de Barcelona, Barcelona, Spain.,Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Caiming Zhang
- Digital Media Technology Key Lab of Shandong Province, Jinan, China.,Department of Software, Shandong University, Jinan, China
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hills, North Carolina.,Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hills, North Carolina
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17
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Pietsch M, Christiaens D, Hutter J, Cordero-Grande L, Price AN, Hughes E, Edwards AD, Hajnal JV, Counsell SJ, Tournier JD. A framework for multi-component analysis of diffusion MRI data over the neonatal period. Neuroimage 2019; 186:321-337. [PMID: 30391562 PMCID: PMC6347572 DOI: 10.1016/j.neuroimage.2018.10.060] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 10/17/2018] [Accepted: 10/22/2018] [Indexed: 12/11/2022] Open
Abstract
We describe a framework for creating a time-resolved group average template of the developing brain using advanced multi-shell high angular resolution diffusion imaging data, for use in group voxel or fixel-wise analysis, atlas-building, and related applications. This relies on the recently proposed multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) technique. We decompose the signal into one isotropic component and two anisotropic components, with response functions estimated from cerebrospinal fluid and white matter in the youngest and oldest participant groups, respectively. We build an orientationally-resolved template of those tissue components from data acquired from 113 babies between 33 and 44 weeks postmenstrual age, imaged as part of the Developing Human Connectome Project. These data were split into weekly groups, and registered to the corresponding group average templates using a previously-proposed non-linear diffeomorphic registration framework, designed to align orientation density functions (ODF). This framework was extended to allow the use of the multiple contrasts provided by the multi-tissue decomposition, and shown to provide superior alignment. Finally, the weekly templates were registered to the same common template to facilitate investigations into the evolution of the different components as a function of age. The resulting multi-tissue atlas provides insights into brain development and accompanying changes in microstructure, and forms the basis for future longitudinal investigations into healthy and pathological white matter maturation.
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Affiliation(s)
- Maximilian Pietsch
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK.
| | - Daan Christiaens
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Anthony N Price
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Emer Hughes
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
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18
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Torrents-Barrena J, Piella G, Masoller N, Gratacós E, Eixarch E, Ceresa M, Ballester MÁG. Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects. Med Image Anal 2018; 51:61-88. [PMID: 30390513 DOI: 10.1016/j.media.2018.10.003] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2017] [Revised: 10/09/2018] [Accepted: 10/18/2018] [Indexed: 12/19/2022]
Abstract
Fetal imaging is a burgeoning topic. New advancements in both magnetic resonance imaging and (3D) ultrasound currently allow doctors to diagnose fetal structural abnormalities such as those involved in twin-to-twin transfusion syndrome, gestational diabetes mellitus, pulmonary sequestration and hypoplasia, congenital heart disease, diaphragmatic hernia, ventriculomegaly, etc. Considering the continued breakthroughs in utero image analysis and (3D) reconstruction models, it is now possible to gain more insight into the ongoing development of the fetus. Best prenatal diagnosis performances rely on the conscious preparation of the clinicians in terms of fetal anatomy knowledge. Therefore, fetal imaging will likely span and increase its prevalence in the forthcoming years. This review covers state-of-the-art segmentation and classification methodologies for the whole fetus and, more specifically, the fetal brain, lungs, liver, heart and placenta in magnetic resonance imaging and (3D) ultrasound for the first time. Potential applications of the aforementioned methods into clinical settings are also inspected. Finally, improvements in existing approaches as well as most promising avenues to new areas of research are briefly outlined.
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Affiliation(s)
- Jordina Torrents-Barrena
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Gemma Piella
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Narcís Masoller
- BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), IDIBAPS, University of Barcelona, Barcelona, Spain; Center for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Eduard Gratacós
- BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), IDIBAPS, University of Barcelona, Barcelona, Spain; Center for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Elisenda Eixarch
- BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), IDIBAPS, University of Barcelona, Barcelona, Spain; Center for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Mario Ceresa
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Miguel Ángel González Ballester
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; ICREA, Barcelona, Spain
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19
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An iterative multi-atlas patch-based approach for cortex segmentation from neonatal MRI. Comput Med Imaging Graph 2018; 70:73-82. [PMID: 30296626 DOI: 10.1016/j.compmedimag.2018.09.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Revised: 08/10/2018] [Accepted: 09/13/2018] [Indexed: 11/21/2022]
Abstract
Brain structure analysis in the newborn is a major health issue. This is especially the case for preterm neonates, in order to obtain predictive information related to the child development. In particular, the cortex is a structure of interest, that can be observed in magnetic resonance imaging (MRI). However, neonatal MRI data present specific properties that make them challenging to process. In this context, multi-atlas approaches constitute an efficient strategy, taking advantage of images processed beforehand. The method proposed in this article relies on such a multi-atlas strategy. More precisely, it uses two paradigms: first, a non-local model based on patches; second, an iterative optimization scheme. Coupling both concepts allows us to consider patches related not only to the image information, but also to the current segmentation. This strategy is compared to other multi-atlas methods proposed in the literature. Experiments on dHCP datasets show that the proposed approach provides robust cortex segmentation results.
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20
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Benkarim OM, Sanroma G, Piella G, Rekik I, Hahner N, Eixarch E, Gonzélez Ballester MA, Shen D, Li G. Revealing Regional Associations of Cortical Folding Alterations with In Utero Ventricular Dilation Using Joint Spectral Embedding. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2018; 11072:620-627. [PMID: 31263804 PMCID: PMC6602588 DOI: 10.1007/978-3-030-00931-1_71] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Fetal ventriculomegaly (VM) is a condition with dilation of one or both lateral ventricles, and is diagnosed as an atrial diameter larger than 10 mm. Evidence of altered cortical folding associated with VM has been shown in the literature. However, existing studies use a holistic approach (i.e., ventricle as a whole) based on diagnosis or ventricular volume, thus failing to reveal the spatially-heterogeneous association patterns between cortex and ventricle. To address this issue, we develop a novel method to identify spatially fine-scaled association maps between cortical development and VM by leveraging vertex-wise correlations between the growth patterns of both ventricular and cortical surfaces in terms of area expansion and curvature information. Our approach comprises multiple steps. In the first step, we define a joint graph Laplacian matrix using cortex-to-ventricle correlations. Next, we propose a spectral embedding of the cortex-to-ventricle graph into a common underlying space where their joint growth patterns are projected. More importantly, in the joint ventricle-cortex space, the vertices of associated regions from both cortical and ventricular surfaces would lie close to each other. In the final step, we perform clustering in the joint embedded space to identify associated sub-regions between cortex and ventricle. Using a dataset of 25 healthy fetuses and 23 fetuses with isolated non-severe VM within the age range of 26-29 gestational weeks, our results show that the proposed approach is able to reveal clinically relevant and meaningful regional associations.
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Affiliation(s)
| | - Gerard Sanroma
- Deutsche Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
| | - Gemma Piella
- BCN Medtech, Universitat Pompeu Fabra, Barcelona, Spain
| | - Islem Rekik
- BASIRA lab, CVIP group, School of Science and Engineering, Computing, University of Dundee, UK
| | - Nadine Hahner
- BCNatal, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain
| | - Elisenda Eixarch
- BCNatal, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain
| | | | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
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21
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Sanroma G, Benkarim OM, Piella G, Lekadir K, Hahner N, Eixarch E, González Ballester MA. Learning to combine complementary segmentation methods for fetal and 6-month infant brain MRI segmentation. Comput Med Imaging Graph 2018; 69:52-59. [PMID: 30176518 DOI: 10.1016/j.compmedimag.2018.08.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 05/21/2018] [Accepted: 08/22/2018] [Indexed: 02/06/2023]
Abstract
Segmentation of brain structures during the pre-natal and early post-natal periods is the first step for subsequent analysis of brain development. Segmentation techniques can be roughly divided into two families. The first, which we denote as registration-based techniques, rely on initial estimates derived by registration to one (or several) templates. The second family, denoted as learning-based techniques, relate imaging (and spatial) features to their corresponding anatomical labels. Each approach has its own qualities and both are complementary to each other. In this paper, we explore two ensembling strategies, namely, stacking and cascading to combine the strengths of both families. We present experiments on segmentation of 6-month infant brains and a cohort of fetuses with isolated non-severe ventriculomegaly (INSVM). INSVM is diagnosed when ventricles are mildly enlarged and no other anomalies are apparent. Prognosis is difficult based solely on the degree of ventricular enlargement. In order to find markers for a more reliable prognosis, we use the resulting segmentations to find abnormalities in the cortical folding of INSVM fetuses. Segmentation results show that either combination strategy outperform all of the individual methods, thus demonstrating the capability of learning systematic combinations that lead to an overall improvement. In particular, the cascading strategy outperforms the ensembling one, the former one obtaining top 5, 7 and 13 results (out of 21 teams) in the segmentation of white matter, gray matter and cerebro-spinal fluid in the iSeg2017 MICCAI Segmentation Challenge. The resulting segmentations reveal that INSVM fetuses have a less convoluted cortex. This points to cortical folding abnormalities as potential markers of later neurodevelopmental outcomes.
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Affiliation(s)
- Gerard Sanroma
- Universitat Pompeu Fabra, Dept. of Information and Communication Technologies, Tànger 122-140, 08018 Barcelona, Spain.
| | - Oualid M Benkarim
- Universitat Pompeu Fabra, Dept. of Information and Communication Technologies, Tànger 122-140, 08018 Barcelona, Spain
| | - Gemma Piella
- Universitat Pompeu Fabra, Dept. of Information and Communication Technologies, Tànger 122-140, 08018 Barcelona, Spain
| | - Karim Lekadir
- Universitat Pompeu Fabra, Dept. of Information and Communication Technologies, Tànger 122-140, 08018 Barcelona, Spain
| | - Nadine Hahner
- Fetal i+D Fetal Medicine Research Center, BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), IDIBAPS, University of Barcelona, Spain
| | - Elisenda Eixarch
- Fetal i+D Fetal Medicine Research Center, BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), IDIBAPS, University of Barcelona, Spain
| | - Miguel A González Ballester
- Universitat Pompeu Fabra, Dept. of Information and Communication Technologies, Tànger 122-140, 08018 Barcelona, Spain; ICREA, Pg. Lluis Companys 23, 08010 Barcelona, Spain
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22
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Xia J, Zhang C, Wang F, Benkarim OM, Sanroma G, Piella G, González Balleste MA, Hahner N, Eixarch E, Shen D, Li G. FETAL CORTICAL PARCELLATION BASED ON GROWTH PATTERNS. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2018; 2018:696-699. [PMID: 30416670 DOI: 10.1109/isbi.2018.8363669] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Dividing the human cerebral cortex into structurally and functionally distinct regions is important in many neuroimaging studies. Although many parcellations have been created for adults, they are not applicable for fetal studies, due to dramatic differences in brain size, shape and folding between adults and fetuses, as well as dynamic growth of fetal brains. To address this issue, we propose a novel method to divide a population of fetal cortical surfaces into distinct regions based on the dynamic growth patterns of cortical properties, which indicate the underlying changes of microstructures. As microstructures determine the molecular organization and functional principles of the cortex, growth patterns enable an accurate definition of distinct regions in development, microstructure, and function. To comprehensively capture the similarities of cortical growth patterns among vertices, we construct two complementary similarity matrices. One is directly based on the growth trajectories of vertices and the other is based on the correlation profiles of vertices' growth trajectories in relation to those of reference points. Then, we nonlinearly fuse these two similarity matrices into a single one, which can better captures both their common and complementary information than by simply averaging them. Finally, based on this fused matrix, we perform spectral clustering to divide fetal cortical surfaces into distinct regions. We have applied our method on 25 normal fetuses from 26 to 29 gestational weeks and generated biologically meaningful parcellations.
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Affiliation(s)
- Jing Xia
- Department of Computer Science and Technology, Shandong University, Shandong, China.,Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Caiming Zhang
- Department of Computer Science and Technology, Shandong University, Shandong, China
| | - Fan Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | | | | | - Gemma Piella
- DTIC, Universitat Pompeu Fabra, Barcelona, Spain
| | | | - Nadine Hahner
- Fetal i+D Fetal Medicine Research Center, BCNatal, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain
| | - Elisenda Eixarch
- Fetal i+D Fetal Medicine Research Center, BCNatal, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
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23
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Computational neuroanatomy of baby brains: A review. Neuroimage 2018; 185:906-925. [PMID: 29574033 DOI: 10.1016/j.neuroimage.2018.03.042] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 02/23/2018] [Accepted: 03/19/2018] [Indexed: 12/12/2022] Open
Abstract
The first postnatal years are an exceptionally dynamic and critical period of structural, functional and connectivity development of the human brain. The increasing availability of non-invasive infant brain MR images provides unprecedented opportunities for accurate and reliable charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, infant brain MR images typically exhibit reduced tissue contrast (especially around 6 months of age), large within-tissue intensity variations, and regionally-heterogeneous, dynamic changes, in comparison with adult brain MR images. Consequently, the existing computational tools developed typically for adult brains are not suitable for infant brain MR image processing. To address these challenges, many infant-tailored computational methods have been proposed for computational neuroanatomy of infant brains. In this review paper, we provide a comprehensive review of the state-of-the-art computational methods for infant brain MRI processing and analysis, which have advanced our understanding of early postnatal brain development. We also summarize publically available infant-dedicated resources, including MRI datasets, computational tools, grand challenges, and brain atlases. Finally, we discuss the limitations in current research and suggest potential future research directions.
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Sanroma G, Benkarim OM, Piella G, Camara O, Wu G, Shen D, Gispert JD, Molinuevo JL, González Ballester MA. Learning non-linear patch embeddings with neural networks for label fusion. Med Image Anal 2018; 44:143-155. [PMID: 29247877 PMCID: PMC5896774 DOI: 10.1016/j.media.2017.11.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 10/05/2017] [Accepted: 11/27/2017] [Indexed: 12/29/2022]
Abstract
In brain structural segmentation, multi-atlas strategies are increasingly being used over single-atlas strategies because of their ability to fit a wider anatomical variability. Patch-based label fusion (PBLF) is a type of such multi-atlas approaches that labels each target point as a weighted combination of neighboring atlas labels, where atlas points with higher local similarity to the target contribute more strongly to label fusion. PBLF can be potentially improved by increasing the discriminative capabilities of the local image similarity measurements. We propose a framework to compute patch embeddings using neural networks so as to increase discriminative abilities of similarity-based weighted voting in PBLF. As particular cases, our framework includes embeddings with different complexities, namely, a simple scaling, an affine transformation, and non-linear transformations. We compare our method with state-of-the-art alternatives in whole hippocampus and hippocampal subfields segmentation experiments using publicly available datasets. Results show that even the simplest versions of our method outperform standard PBLF, thus evidencing the benefits of discriminative learning. More complex transformation models tended to achieve better results than simpler ones, obtaining a considerable increase in average Dice score compared to standard PBLF.
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Affiliation(s)
- Gerard Sanroma
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Tànger 122-140, Barcelona 08018, Spain
| | - Oualid M. Benkarim
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Tànger 122-140, Barcelona 08018, Spain
| | - Gemma Piella
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Tànger 122-140, Barcelona 08018, Spain
| | - Oscar Camara
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Tànger 122-140, Barcelona 08018, Spain
| | - Guorong Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, 102 Mason Farm Rd., NC 27599, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, 102 Mason Farm Rd., NC 27599, USA
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Juan D. Gispert
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Wellington 30, Barcelona 08005 Spain
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Wellington 30, Barcelona 08005 Spain
| | - Miguel A. González Ballester
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Tànger 122-140, Barcelona 08018, Spain
- ICREA, Pg. Lluis Companys 23, Barcelona 08010 Spain
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25
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Benkarim OM, Hahner N, Piella G, Gratacos E, González Ballester MA, Eixarch E, Sanroma G. Cortical folding alterations in fetuses with isolated non-severe ventriculomegaly. NEUROIMAGE-CLINICAL 2018; 18:103-114. [PMID: 29387528 PMCID: PMC5790022 DOI: 10.1016/j.nicl.2018.01.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Revised: 11/23/2017] [Accepted: 01/09/2018] [Indexed: 11/15/2022]
Abstract
Neuroimaging of brain diseases plays a crucial role in understanding brain abnormalities and early diagnosis. Of great importance is the study of brain abnormalities in utero and the assessment of deviations in case of maldevelopment. In this work, brain magnetic resonance images from 23 isolated non-severe ventriculomegaly (INSVM) fetuses and 25 healthy controls between 26 and 29 gestational weeks were used to identify INSVM-related cortical folding deviations from normative development. Since these alterations may reflect abnormal neurodevelopment, our working hypothesis is that markers of cortical folding can provide cues to improve the prediction of later neurodevelopmental problems in INSVM subjects. We analyzed the relationship of ventricular enlargement with cortical folding alterations in a regional basis using several curvature-based measures describing the folding of each cortical region. Statistical analysis (global and hemispheric) and sparse linear regression approaches were then used to find the cortical regions whose folding is associated with ventricular dilation. Results from both approaches were in great accordance, showing a significant cortical folding decrease in the insula, posterior part of the temporal lobe and occipital lobe. Moreover, compared to the global analysis, stronger ipsilateral associations of ventricular enlargement with reduced cortical folding were encountered by the hemispheric analysis. Our findings confirm and extend previous studies by identifying various cortical regions and emphasizing ipsilateral effects of ventricular enlargement in altered folding. This suggests that INSVM is an indicator of altered cortical development, and moreover, cortical regions with reduced folding constitute potential prognostic biomarkers to be used in follow-up studies to decipher the outcome of INSVM fetuses.
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Affiliation(s)
| | - Nadine Hahner
- Fetal i+D Fetal Medicine Research Center, BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), Institut Clínic de Ginecologia, Obstetricia i Neonatologia, IDIBAPS, Universitat de Barcelona, Barcelona, Spain; Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Gemma Piella
- DTIC, Universitat Pompeu Fabra, Barcelona, Spain
| | - Eduard Gratacos
- Fetal i+D Fetal Medicine Research Center, BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), Institut Clínic de Ginecologia, Obstetricia i Neonatologia, IDIBAPS, Universitat de Barcelona, Barcelona, Spain; Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | | | - Elisenda Eixarch
- Fetal i+D Fetal Medicine Research Center, BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), Institut Clínic de Ginecologia, Obstetricia i Neonatologia, IDIBAPS, Universitat de Barcelona, Barcelona, Spain; Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain.
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