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Schneider A, Munoz C, Hua A, Ellis S, Jeljeli S, Kunze KP, Neji R, Reader AJ, Reyes E, Ismail TF, Botnar RM, Prieto C. Non-rigid motion-compensated 3D whole-heart T 2 mapping in a hybrid 3T PET-MR system. Magn Reson Med 2024; 91:1951-1964. [PMID: 38181169 DOI: 10.1002/mrm.29973] [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: 06/07/2023] [Revised: 11/21/2023] [Accepted: 11/26/2023] [Indexed: 01/07/2024]
Abstract
PURPOSE Simultaneous PET-MRI improves inflammatory cardiac disease diagnosis. However, challenges persist in respiratory motion and mis-registration between free-breathing 3D PET and 2D breath-held MR images. We propose a free-breathing non-rigid motion-compensated 3D T2 -mapping sequence enabling whole-heart myocardial tissue characterization in a hybrid 3T PET-MR system and provides non-rigid respiratory motion fields to correct also simultaneously acquired PET data. METHODS Free-breathing 3D whole-heart T2 -mapping was implemented on a hybrid 3T PET-MRI system. Three datasets were acquired with different T2 -preparation modules (0, 28, 55 ms) using 3-fold undersampled variable-density Cartesian trajectory. Respiratory motion was estimated via virtual 3D image navigators, enabling multi-contrast non-rigid motion-corrected MR reconstruction. T2 -maps were computed using dictionary-matching. Approach was tested in phantom, 8 healthy subjects, 14 MR only and 2 PET-MR patients with suspected cardiac disease and compared with spin echo reference (phantom) and clinical 2D T2 -mapping (in-vivo). RESULTS Phantom results show a high correlation (R2 = 0.996) between proposed approach and gold standard 2D T2 mapping. In-vivo 3D T2 -mapping average values in healthy subjects (39.0 ± 1.4 ms) and patients (healthy tissue) (39.1 ± 1.4 ms) agree with conventional 2D T2 -mapping (healthy = 38.6 ± 1.2 ms, patients = 40.3 ± 1.7 ms). Bland-Altman analysis reveals bias of 1.8 ms and 95% limits of agreement (LOA) of -2.4-6 ms for healthy subjects, and bias of 1.3 ms and 95% LOA of -1.9 to 4.6 ms for patients. CONCLUSION Validated efficient 3D whole-heart T2 -mapping at hybrid 3T PET-MRI provides myocardial inflammation characterization and non-rigid respiratory motion fields for simultaneous PET data correction. Comparable T2 values were achieved with both 3D and 2D methods. Improved image quality was observed in the PET images after MR-based motion correction.
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Affiliation(s)
- Alina Schneider
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Camila Munoz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Alina Hua
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Sam Ellis
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Sami Jeljeli
- PET Centre, St Thomas' Hospital, King's College London & Guys and St Thomas' NHS Foundation Trust, London, UK
| | - Karl P Kunze
- MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Andrew J Reader
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Eliana Reyes
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Tevfik F Ismail
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millenium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millenium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile
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Aizaz M, van der Pol JAJ, Schneider A, Munoz C, Holtackers RJ, van Cauteren Y, van Langen H, Meeder JG, Rahel BM, Wierts R, Botnar RM, Prieto C, Moonen RPM, Kooi ME. Extended MRI-based PET motion correction for cardiac PET/MRI. EJNMMI Phys 2024; 11:36. [PMID: 38581561 PMCID: PMC10998820 DOI: 10.1186/s40658-024-00637-z] [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: 08/22/2023] [Accepted: 03/25/2024] [Indexed: 04/08/2024] Open
Abstract
PURPOSE A 2D image navigator (iNAV) based 3D whole-heart sequence has been used to perform MRI and PET non-rigid respiratory motion correction for hybrid PET/MRI. However, only the PET data acquired during the acquisition of the 3D whole-heart MRI is corrected for respiratory motion. This study introduces and evaluates an MRI-based respiratory motion correction method of the complete PET data. METHODS Twelve oncology patients scheduled for an additional cardiac 18F-Fluorodeoxyglucose (18F-FDG) PET/MRI and 15 patients with coronary artery disease (CAD) scheduled for cardiac 18F-Choline (18F-FCH) PET/MRI were included. A 2D iNAV recorded the respiratory motion of the myocardium during the 3D whole-heart coronary MR angiography (CMRA) acquisition (~ 10 min). A respiratory belt was used to record the respiratory motion throughout the entire PET/MRI examination (~ 30-90 min). The simultaneously acquired iNAV and respiratory belt signal were used to divide the acquired PET data into 4 bins. The binning was then extended for the complete respiratory belt signal. Data acquired at each bin was reconstructed and combined using iNAV-based motion fields to create a respiratory motion-corrected PET image. Motion-corrected (MC) and non-motion-corrected (NMC) datasets were compared. Gating was also performed to correct cardiac motion. The SUVmax and TBRmax values were calculated for the myocardial wall or a vulnerable coronary plaque for the 18F-FDG and 18F-FCH datasets, respectively. RESULTS A pair-wise comparison showed that the SUVmax and TBRmax values of the motion corrected (MC) datasets were significantly higher than those for the non-motion-corrected (NMC) datasets (8.2 ± 1.0 vs 7.5 ± 1.0, p < 0.01 and 1.9 ± 0.2 vs 1.2 ± 0.2, p < 0.01, respectively). In addition, the SUVmax and TBRmax of the motion corrected and gated (MC_G) reconstructions were also higher than that of the non-motion-corrected but gated (NMC_G) datasets, although for the TBRmax this difference was not statistically significant (9.6 ± 1.3 vs 9.1 ± 1.2, p = 0.02 and 2.6 ± 0.3 vs 2.4 ± 0.3, p = 0.16, respectively). The respiratory motion-correction did not lead to a change in the signal to noise ratio. CONCLUSION The proposed respiratory motion correction method for hybrid PET/MRI improved the image quality of cardiovascular PET scans by increased SUVmax and TBRmax values while maintaining the signal-to-noise ratio. Trial registration METC162043 registered 01/03/2017.
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Affiliation(s)
- Mueez Aizaz
- CARIM, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Jochem A J van der Pol
- CARIM, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Alina Schneider
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Camila Munoz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Robert J Holtackers
- CARIM, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Yvonne van Cauteren
- CARIM, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
- Department of Cardiology, VieCuri Medical Centre, Venlo, The Netherlands
| | - Herman van Langen
- Department of Medical Physics and Devices, VieCuri Medical Centre, Venlo, The Netherlands
| | - Joan G Meeder
- Department of Cardiology, VieCuri Medical Centre, Venlo, The Netherlands
| | - Braim M Rahel
- Department of Cardiology, VieCuri Medical Centre, Venlo, The Netherlands
| | - Roel Wierts
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millenium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile
- Instituto de Ingeniería Biológica y Médica, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millenium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile
| | - Rik P M Moonen
- CARIM, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - M Eline Kooi
- CARIM, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands.
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands.
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Phair A, Fotaki A, Felsner L, Fletcher TJ, Qi H, Botnar RM, Prieto C. A motion-corrected deep-learning reconstruction framework for accelerating whole-heart magnetic resonance imaging in patients with congenital heart disease. J Cardiovasc Magn Reson 2024; 26:101039. [PMID: 38521391 PMCID: PMC10993190 DOI: 10.1016/j.jocmr.2024.101039] [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: 11/09/2023] [Revised: 02/16/2024] [Accepted: 03/14/2024] [Indexed: 03/25/2024] Open
Abstract
BACKGROUND Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment and management of adult patients with congenital heart disease (CHD). However, conventional techniques for three-dimensional (3D) whole-heart acquisition involve long and unpredictable scan times and methods that accelerate scans via k-space undersampling often rely on long iterative reconstructions. Deep-learning-based reconstruction methods have recently attracted much interest due to their capacity to provide fast reconstructions while often outperforming existing state-of-the-art methods. In this study, we sought to adapt and validate a non-rigid motion-corrected model-based deep learning (MoCo-MoDL) reconstruction framework for 3D whole-heart MRI in a CHD patient cohort. METHODS The previously proposed deep-learning reconstruction framework MoCo-MoDL, which incorporates a non-rigid motion-estimation network and a denoising regularization network within an unrolled iterative reconstruction, was trained in an end-to-end manner using 39 CHD patient datasets. Once trained, the framework was evaluated in eight CHD patient datasets acquired with seven-fold prospective undersampling. Reconstruction quality was compared with the state-of-the-art non-rigid motion-corrected patch-based low-rank reconstruction method (NR-PROST) and against reference images (acquired with three-or-four-fold undersampling and reconstructed with NR-PROST). RESULTS Seven-fold undersampled scan times were 2.1 ± 0.3 minutes and reconstruction times were ∼30 seconds, approximately 240 times faster than an NR-PROST reconstruction. Image quality comparable to the reference images was achieved using the proposed MoCo-MoDL framework, with no statistically significant differences found in any of the assessed quantitative or qualitative image quality measures. Additionally, expert image quality scores indicated the MoCo-MoDL reconstructions were consistently of a higher quality than the NR-PROST reconstructions of the same data, with the differences in 12 of the 22 scores measured for individual vascular structures found to be statistically significant. CONCLUSION The MoCo-MoDL framework was applied to an adult CHD patient cohort, achieving good quality 3D whole-heart images from ∼2-minute scans with reconstruction times of ∼30 seconds.
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Affiliation(s)
- Andrew Phair
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Anastasia Fotaki
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Lina Felsner
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Thomas J Fletcher
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Haikun Qi
- School of Biomedical Engineering, Shanghai Tech University, Shanghai, China
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Instituto de Ingeniería Biológica y Médica, Pontificia Universidad Católica de Chile, Santiago, Chile; Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile; Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile; Technical University of Munich, Institute of Advanced Study, Munich, Germany
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile; Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile.
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Nadel J, Wang X, Saha P, Bongers A, Tumanov S, Giannotti N, Chen W, Vigder N, Chowdhury MM, da Cruz GL, Velasco C, Prieto C, Jabbour A, Botnar RM, Stocker R, Phinikaridou A. Molecular magnetic resonance imaging of myeloperoxidase activity identifies culprit lesions and predicts future atherothrombosis. Eur Heart J Imaging Methods Pract 2024; 2:qyae004. [PMID: 38370393 PMCID: PMC10870993 DOI: 10.1093/ehjimp/qyae004] [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] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 01/22/2024] [Indexed: 02/20/2024]
Abstract
Aims Unstable atherosclerotic plaques have increased activity of myeloperoxidase (MPO). We examined whether molecular magnetic resonance imaging (MRI) of intraplaque MPO activity predicts future atherothrombosis in rabbits and correlates with ruptured human atheroma. Methods and results Plaque MPO activity was assessed in vivo in rabbits (n = 12) using the MPO-gadolinium (Gd) probe at 8 and 12 weeks after induction of atherosclerosis and before pharmacological triggering of atherothrombosis. Excised plaques were used to confirm MPO activity by liquid chromatography-tandem mass spectrometry (LC-MSMS) and to determine MPO distribution by histology. MPO activity was higher in plaques that caused post-trigger atherothrombosis than plaques that did not. Among the in vivo MRI metrics, the plaques' R1 relaxation rate after administration of MPO-Gd was the best predictor of atherothrombosis. MPO activity measured in human carotid endarterectomy specimens (n = 30) by MPO-Gd-enhanced MRI was correlated with in vivo patient MRI and histological plaque phenotyping, as well as LC-MSMS. MPO-Gd retention measured as the change in R1 relaxation from baseline was significantly greater in histologic and MRI-graded American Heart Association (AHA) type VI than type III-V plaques. This association was confirmed by comparing AHA grade to MPO activity determined by LC-MSMS. Conclusion We show that elevated intraplaque MPO activity detected by molecular MRI employing MPO-Gd predicts future atherothrombosis in a rabbit model and detects ruptured human atheroma, strengthening the translational potential of this approach to prospectively detect high-risk atherosclerosis.
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Affiliation(s)
- James Nadel
- Heart Research Institute, Arterial Inflammation and Redox Biology Group, 7 Eliza St, Newtown, Sydney, NSW 2042, Australia
- Department of Cardiology, St Vincent’s Hospital, Sydney, NSW, Australia
- Department of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Xiaoying Wang
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Prakash Saha
- Academic Department of Surgery, Cardiovascular Division, King’s College London, London, UK
| | - André Bongers
- Biological Resources Imaging Laboratory, University of New South Wales, Sydney, NSW, Australia
| | - Sergey Tumanov
- Heart Research Institute, Arterial Inflammation and Redox Biology Group, 7 Eliza St, Newtown, Sydney, NSW 2042, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Nicola Giannotti
- Medical Imaging Science, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Weiyu Chen
- Heart Research Institute, Arterial Inflammation and Redox Biology Group, 7 Eliza St, Newtown, Sydney, NSW 2042, Australia
| | - Niv Vigder
- Heart Research Institute, Arterial Inflammation and Redox Biology Group, 7 Eliza St, Newtown, Sydney, NSW 2042, Australia
| | | | | | - Carlos Velasco
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Claudia Prieto
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Pontificia Universidad Católica de Chile, Institute for Biological and Medical Engineering, Santiago, Chile
| | - Andrew Jabbour
- Department of Cardiology, St Vincent’s Hospital, Sydney, NSW, Australia
- Department of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - René M Botnar
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Pontificia Universidad Católica de Chile, Institute for Biological and Medical Engineering, Santiago, Chile
- King’s BHF Centre of Research Excellence, London, UK
| | - Roland Stocker
- Heart Research Institute, Arterial Inflammation and Redox Biology Group, 7 Eliza St, Newtown, Sydney, NSW 2042, Australia
| | - Alkystis Phinikaridou
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- King’s BHF Centre of Research Excellence, London, UK
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Fotaki A, Pushparajah K, Rush C, Munoz C, Velasco C, Neji R, Kunze KP, Botnar RM, Prieto C. Highly efficient free-breathing 3D whole-heart imaging in 3-min: single center study in adults with congenital heart disease. J Cardiovasc Magn Reson 2023; 26:100008. [PMID: 38194762 DOI: 10.1016/j.jocmr.2023.100008] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 12/10/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND Three dimensional, whole-heart (3DWH) MRI is an established non-invasive imaging modality in patients with congenital heart disease (CHD) for the diagnosis of cardiovascular morphology and for clinical decision making. Current techniques utilise diaphragmatic navigation (dNAV) for respiratory motion correction and gating and are frequently limited by long acquisition times. This study proposes and evaluates the diagnostic performance of a respiratory gating-free framework, which considers respiratory image-based navigation (iNAV), and highly accelerated variable density Cartesian sampling in concert with non-rigid motion correction and low-rank patch-based denoising (iNAV-3DWH-PROST). The method is compared to the clinical dNAV-3DWH sequence in adult patients with CHD. METHODS In this prospective single center study, adult patients with CHD who underwent the clinical dNAV-3DWH MRI were also scanned with the iNAV-3DWH-PROST. Diagnostic confidence (4-point Likert scale) and diagnostic accuracy for common cardiovascular lesions was assessed by three readers. Scan times and diagnostic confidence were compared using the Wilcoxon-signed rank test. Co-axial vascular dimensions at three anatomic landmarks were measured, and agreement between the research and the corresponding clinical sequence was assessed with Bland-Altman analysis. RESULTS The study included 60 participants (mean age ± [SD]: 33 ± 14 years; 36 men). The mean acquisition time of iNAV-3DWH-PROST was significantly lower compared with the conventional clinical sequence (3.1 ± 0.9 min vs 13.9 ± 3.9 min, p < 0.0001). Diagnostic confidence was higher for the iNAV-3DWH-PROST sequence compared with the clinical sequence (3.9 ± 0.2 vs 3.4 ± 0.8, p < 0.001), however there was no significant difference in diagnostic accuracy. Narrow limits of agreement and mean bias less than 0.08 cm were found between the research and the clinical vascular measurements. CONCLUSIONS The iNAV-3DWH-PROST framework provides efficient, high quality and robust 3D whole-heart imaging in significantly shorter scan time compared to the standard clinical sequence.
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Affiliation(s)
- Anastasia Fotaki
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, SE1 7EH London, United Kingdom; Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom.
| | - Kuberan Pushparajah
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, SE1 7EH London, United Kingdom; Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Christopher Rush
- Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Camila Munoz
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, SE1 7EH London, United Kingdom
| | - Carlos Velasco
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, SE1 7EH London, United Kingdom
| | - Radhouene Neji
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, SE1 7EH London, United Kingdom; MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - Karl P Kunze
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, SE1 7EH London, United Kingdom; MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - René M Botnar
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, SE1 7EH London, United Kingdom; Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile; Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile; Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile; Institute for Advanced Study, Technical University of Munich, Lichtenbergstrasse 2 a, D-85748 Garching, Germany
| | - Claudia Prieto
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, SE1 7EH London, United Kingdom; Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile; Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
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Wood G, Pedersen AU, Kunze KP, Neji R, Hajhosseiny R, Wetzl J, Yoon SS, Schmidt M, Nørgaard BL, Prieto C, Botnar RM, Kim WY. Automated detection of cardiac rest period for trigger delay calculation for image-based navigator coronary magnetic resonance angiography. J Cardiovasc Magn Reson 2023; 25:52. [PMID: 37779192 PMCID: PMC10544388 DOI: 10.1186/s12968-023-00962-9] [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: 06/28/2023] [Accepted: 09/12/2023] [Indexed: 10/03/2023] Open
Abstract
BACKGROUND Coronary magnetic resonance angiography (coronary MRA) is increasingly being considered as a clinically viable method to investigate coronary artery disease (CAD). Accurate determination of the trigger delay to place the acquisition window within the quiescent part of the cardiac cycle is critical for coronary MRA in order to reduce cardiac motion. This is currently reliant on operator-led decision making, which can negatively affect consistency of scan acquisition. Recently developed deep learning (DL) derived software may overcome these issues by automation of cardiac rest period detection. METHODS Thirty individuals (female, n = 10) were investigated using a 0.9 mm isotropic image-navigator (iNAV)-based motion-corrected coronary MRA sequence. Each individual was scanned three times utilising different strategies for determination of the optimal trigger delay: (1) the DL software, (2) an experienced operator decision, and (3) a previously utilised formula for determining the trigger delay. Methodologies were compared using custom-made analysis software to assess visible coronary vessel length and coronary vessel sharpness for the entire vessel length and the first 4 cm of each vessel. RESULTS There was no difference in image quality between any of the methodologies for determination of the optimal trigger delay, as assessed by visible coronary vessel length, coronary vessel sharpness for each entire vessel and vessel sharpness for the first 4 cm of the left mainstem, left anterior descending or right coronary arteries. However, vessel length of the left circumflex was slightly greater using the formula method. The time taken to calculate the trigger delay was significantly lower for the DL-method as compared to the operator-led approach (106 ± 38.0 s vs 168 ± 39.2 s, p < 0.01, 95% CI of difference 25.5-98.1 s). CONCLUSIONS Deep learning-derived automated software can effectively and efficiently determine the optimal trigger delay for acquisition of coronary MRA and thus may simplify workflow and improve reproducibility.
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Affiliation(s)
- Gregory Wood
- Department of Cardiology, Aarhus University Hospital, Palle Juul Jensens Boulevard 99, 8200, Aarhus N, Denmark.
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
| | - Alexandra Uglebjerg Pedersen
- Department of Cardiology, Aarhus University Hospital, Palle Juul Jensens Boulevard 99, 8200, Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Karl P Kunze
- MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Radhouene Neji
- MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Reza Hajhosseiny
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Jens Wetzl
- Cardiovascular MR Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Seung Su Yoon
- Cardiovascular MR Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Michaela Schmidt
- Cardiovascular MR Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Bjarne Linde Nørgaard
- Department of Cardiology, Aarhus University Hospital, Palle Juul Jensens Boulevard 99, 8200, Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millenium Institute for Intelligent Healthcare Engineering, Santiago, Chile
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
- Instituto de Ingeniería Biológica y Médica, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Advanced Study, Technical University of Munich, Garching, Germany
- Millenium Institute for Intelligent Healthcare Engineering, Santiago, Chile
| | - Won Yong Kim
- Department of Cardiology, Aarhus University Hospital, Palle Juul Jensens Boulevard 99, 8200, Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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Mézquita AJV, Biavati F, Falk V, Alkadhi H, Hajhosseiny R, Maurovich-Horvat P, Manka R, Kozerke S, Stuber M, Derlin T, Channon KM, Išgum I, Coenen A, Foellmer B, Dey D, Volleberg RHJA, Meinel FG, Dweck MR, Piek JJ, van de Hoef T, Landmesser U, Guagliumi G, Giannopoulos AA, Botnar RM, Khamis R, Williams MC, Newby DE, Dewey M. Clinical quantitative coronary artery stenosis and coronary atherosclerosis imaging: a Consensus Statement from the Quantitative Cardiovascular Imaging Study Group. Nat Rev Cardiol 2023; 20:696-714. [PMID: 37277608 DOI: 10.1038/s41569-023-00880-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/19/2023] [Indexed: 06/07/2023]
Abstract
The detection and characterization of coronary artery stenosis and atherosclerosis using imaging tools are key for clinical decision-making in patients with known or suspected coronary artery disease. In this regard, imaging-based quantification can be improved by choosing the most appropriate imaging modality for diagnosis, treatment and procedural planning. In this Consensus Statement, we provide clinical consensus recommendations on the optimal use of different imaging techniques in various patient populations and describe the advances in imaging technology. Clinical consensus recommendations on the appropriateness of each imaging technique for direct coronary artery visualization were derived through a three-step, real-time Delphi process that took place before, during and after the Second International Quantitative Cardiovascular Imaging Meeting in September 2022. According to the Delphi survey answers, CT is the method of choice to rule out obstructive stenosis in patients with an intermediate pre-test probability of coronary artery disease and enables quantitative assessment of coronary plaque with respect to dimensions, composition, location and related risk of future cardiovascular events, whereas MRI facilitates the visualization of coronary plaque and can be used in experienced centres as a radiation-free, second-line option for non-invasive coronary angiography. PET has the greatest potential for quantifying inflammation in coronary plaque but SPECT currently has a limited role in clinical coronary artery stenosis and atherosclerosis imaging. Invasive coronary angiography is the reference standard for stenosis assessment but cannot characterize coronary plaques. Finally, intravascular ultrasonography and optical coherence tomography are the most important invasive imaging modalities for the identification of plaques at high risk of rupture. The recommendations made in this Consensus Statement will help clinicians to choose the most appropriate imaging modality on the basis of the specific clinical scenario, individual patient characteristics and the availability of each imaging modality.
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Affiliation(s)
| | - Federico Biavati
- Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Volkmar Falk
- Department of Cardiothoracic and Vascular Surgery, Deutsches Herzzentrum der Charité (DHZC), Charité - Universitätsmedizin Berlin, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research) Partner Site, Berlin, Germany
- Department of Health Science and Technology, ETH Zurich, Zurich, Switzerland
| | - Hatem Alkadhi
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Reza Hajhosseiny
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Pál Maurovich-Horvat
- Department of Radiology, Medical Imaging Center, Semmelweis University, Budapest, Hungary
| | - Robert Manka
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Cardiology, University Heart Center, University Hospital Zurich, Zurich, Switzerland
| | - Sebastian Kozerke
- Institute for Biomedical Engineering, ETH Zurich, University of Zurich, Zurich, Switzerland
| | - Matthias Stuber
- Department of Radiology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Thorsten Derlin
- Department of Nuclear Medicine, Hannover Medical School, Hannover, Germany
| | - Keith M Channon
- Radcliffe Department of Medicine, University of Oxford and Oxford University Hospitals, Oxford, UK
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Adriaan Coenen
- Department of Radiology, Erasmus University, Rotterdam, Netherlands
| | - Bernhard Foellmer
- Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Damini Dey
- Departments of Biomedical Sciences and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Rick H J A Volleberg
- Department of Cardiology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Felix G Meinel
- Department of Radiology, University Medical Centre Rostock, Rostock, Germany
| | - Marc R Dweck
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Jan J Piek
- Department of Clinical and Experimental Cardiology and Cardiovascular Sciences, Amsterdam UMC, Heart Center, University of Amsterdam, Amsterdam, Netherlands
| | - Tim van de Hoef
- Department of Cardiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Ulf Landmesser
- DZHK (German Centre for Cardiovascular Research) Partner Site, Berlin, Germany
- Department of Cardiology, Deutsches Herzzentrum der Charité (DHZC), Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Giulio Guagliumi
- Division of Cardiology, IRCCS Galeazzi Sant'Ambrogio Hospital, Milan, Italy
| | - Andreas A Giannopoulos
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
| | - Ramzi Khamis
- National Heart and Lung Institute, Imperial College London, London, UK
| | | | - David E Newby
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Marc Dewey
- Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
- DZHK (German Centre for Cardiovascular Research) Partner Site, Berlin, Germany.
- Deutsches Herzzentrum der Charité (DHZC), Charité - Universitätsmedizin Berlin, Berlin, Germany.
- Berlin Institute of Health, Campus Charité Mitte, Berlin, Germany.
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Munoz C, Fotaki A, Hua A, Hajhosseiny R, Kunze KP, Ismail TF, Neji R, Pushparajah K, Botnar RM, Prieto C. Simultaneous Highly Efficient Contrast-Free Lumen and Vessel Wall MR Imaging for Anatomical Assessment of Aortic Disease. J Magn Reson Imaging 2023; 58:1110-1122. [PMID: 36757267 PMCID: PMC10946808 DOI: 10.1002/jmri.28613] [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: 09/20/2022] [Revised: 12/24/2022] [Accepted: 12/27/2022] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND Bright-blood lumen and black-blood vessel wall imaging are required for the comprehensive assessment of aortic disease. These images are usually acquired separately, resulting in long examinations and potential misregistration between images. PURPOSE To characterize the performance of an accelerated and respiratory motion-compensated three-dimensional (3D) cardiac MRI technique for simultaneous contrast-free aortic lumen and vessel wall imaging with an interleaved T2 and inversion recovery prepared sequence (iT2Prep-BOOST). STUDY TYPE Prospective. POPULATION A total of 30 consecutive patients with aortopathy referred for a clinically indicated cardiac MRI examination (9 females, mean age ± standard deviation: 32 ± 12 years). FIELD STRENGTH/SEQUENCE 1.5-T; bright-blood MR angiography (diaphragmatic navigator-gated T2-prepared 3D balanced steady-state free precession [bSSFP], T2Prep-bSSFP), breath-held black-blood two-dimensional (2D) half acquisition single-shot turbo spin echo (HASTE), and 3D bSSFP iT2Prep-BOOST. ASSESSMENT iT2Prep-BOOST bright-blood images were compared to T2prep-bSSFP images in terms of aortic vessel dimensions, lumen-to-myocardium contrast ratio (CR), and image quality (diagnostic confidence, vessel sharpness and presence of artifacts, assessed by three cardiologists on a 4-point scale, 1: nondiagnostic to 4: excellent). The iT2Prep-BOOST black-blood images were compared to 2D HASTE images for quantification of wall thickness. A visual comparison between computed tomography (CT) and iT2Prep-BOOST was performed in a patient with chronic aortic dissection. STATISTICAL TESTS Paired t-tests, Wilcoxon signed-rank tests, intraclass correlation coefficient (ICC), Bland-Altman analysis. A P value < 0.05 was considered statistically significant. RESULTS Bright-blood iT2Prep-BOOST resulted in significantly improved image quality (mean ± standard deviation 3.8 ± 0.5 vs. 3.3 ± 0.8) and CR (2.9 ± 0.8 vs. 1.8 ± 0.5) compared with T2Prep-bSSFP, with a shorter scan time (7.8 ± 1.7 minutes vs. 12.9 ± 3.4 minutes) while providing a complementary 3D black-blood image. Aortic lumen diameter and vessel wall thickness measurements in bright-blood and black-blood images were in good agreement with T2Prep-bSSFP and HASTE images (<0.02 cm and <0.005 cm bias, respectively) and good intrareader (ICC > 0.96) and interreader (ICC > 0.94) agreement was observed for all measurements. DATA CONCLUSION iT2Prep-BOOST might enable time-efficient simultaneous bright- and black-blood aortic imaging, with improved image quality compared to T2Prep-bSSFP and HASTE imaging, and comparable measurements for aortic wall and lumen dimensions. EVIDENCE LEVEL 2. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Camila Munoz
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Anastasia Fotaki
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Alina Hua
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Reza Hajhosseiny
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Karl P. Kunze
- MR Research CollaborationsSiemens Healthcare LimitedFrimleyUK
| | - Tevfik F. Ismail
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- MR Research CollaborationsSiemens Healthcare LimitedFrimleyUK
| | - Kuberan Pushparajah
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - René M. Botnar
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Escuela de Ingeniería, Pontificia Universidad Católica de ChileSantiagoChile
- Instituto de Ingeniería Biológica y Médica, Pontificia Universidad Católica de ChileSantiagoChile
- Millenium Institute for Intelligent Healthcare Engineering iHEALTHSantiagoChile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Escuela de Ingeniería, Pontificia Universidad Católica de ChileSantiagoChile
- Instituto de Ingeniería Biológica y Médica, Pontificia Universidad Católica de ChileSantiagoChile
- Millenium Institute for Intelligent Healthcare Engineering iHEALTHSantiagoChile
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Saha P, Gutmann C, Kingdon J, Dregan A, Bertolaccini L, Grover SP, Patel AS, Modarai B, Lyons O, Schulz C, Andia ME, Phinikaridou A, Botnar RM, Smith A. Venous Thrombosis Accelerates Atherosclerosis in Mice. Circulation 2023; 147:1945-1947. [PMID: 37335825 DOI: 10.1161/circulationaha.123.064268] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/21/2023]
Affiliation(s)
- Prakash Saha
- British Heart Foundation Centre of Research Excellence (P.S., C.G., J.K., L.B., A.S.P., B.M., O.L., A.P., R.M.B., A.S.), King's College London, United Kingdom
- School of Cardiovascular and Metabolic Medicine & Sciences (P.S., C.G., J.K., L.B., A.S.P., B.M., O.L., A.S.), King's College London, United Kingdom
| | - Clemens Gutmann
- British Heart Foundation Centre of Research Excellence (P.S., C.G., J.K., L.B., A.S.P., B.M., O.L., A.P., R.M.B., A.S.), King's College London, United Kingdom
- School of Cardiovascular and Metabolic Medicine & Sciences (P.S., C.G., J.K., L.B., A.S.P., B.M., O.L., A.S.), King's College London, United Kingdom
- Division of Cardiology, Medical University of Vienna, Austria (C.G.)
| | - Jack Kingdon
- British Heart Foundation Centre of Research Excellence (P.S., C.G., J.K., L.B., A.S.P., B.M., O.L., A.P., R.M.B., A.S.), King's College London, United Kingdom
- School of Cardiovascular and Metabolic Medicine & Sciences (P.S., C.G., J.K., L.B., A.S.P., B.M., O.L., A.S.), King's College London, United Kingdom
| | - Alexandru Dregan
- Institute of Psychiatry, Psychology, and Neuroscience, Department of Psychological Medicine (A.D.), King's College London, United Kingdom
| | - Laura Bertolaccini
- School of Cardiovascular and Metabolic Medicine & Sciences (P.S., C.G., J.K., L.B., A.S.P., B.M., O.L., A.S.), King's College London, United Kingdom
| | - Steven P Grover
- University of North Carolina Blood Research Center, Division of Hematology and Oncology, Department of Medicine, University of North Carolina at Chapel Hill (S.P.G.)
| | - Ashish S Patel
- British Heart Foundation Centre of Research Excellence (P.S., C.G., J.K., L.B., A.S.P., B.M., O.L., A.P., R.M.B., A.S.), King's College London, United Kingdom
- School of Cardiovascular and Metabolic Medicine & Sciences (P.S., C.G., J.K., L.B., A.S.P., B.M., O.L., A.S.), King's College London, United Kingdom
| | - Bijan Modarai
- British Heart Foundation Centre of Research Excellence (P.S., C.G., J.K., L.B., A.S.P., B.M., O.L., A.P., R.M.B., A.S.), King's College London, United Kingdom
- School of Cardiovascular and Metabolic Medicine & Sciences (P.S., C.G., J.K., L.B., A.S.P., B.M., O.L., A.S.), King's College London, United Kingdom
| | - Oliver Lyons
- British Heart Foundation Centre of Research Excellence (P.S., C.G., J.K., L.B., A.S.P., B.M., O.L., A.P., R.M.B., A.S.), King's College London, United Kingdom
- School of Cardiovascular and Metabolic Medicine & Sciences (P.S., C.G., J.K., L.B., A.S.P., B.M., O.L., A.S.), King's College London, United Kingdom
- Department of Surgery, University of Otago, Christchurch (O.L.)
| | - Christian Schulz
- Medizinische Klinik und Poliklinik I, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich, Germany (C.S.)
| | - Marcelo E Andia
- Biomedical Imaging Centre, School of Medicine (M.E.A), Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH), Santiago, Chile (M.E.A, R.M.B)
| | - Alkystis Phinikaridou
- British Heart Foundation Centre of Research Excellence (P.S., C.G., J.K., L.B., A.S.P., B.M., O.L., A.P., R.M.B., A.S.), King's College London, United Kingdom
- School of Biomedical Engineering and Imaging Sciences (A.P., R.M.B.). King's College London, United Kingdom
| | - René M Botnar
- British Heart Foundation Centre of Research Excellence (P.S., C.G., J.K., L.B., A.S.P., B.M., O.L., A.P., R.M.B., A.S.), King's College London, United Kingdom
- School of Biomedical Engineering and Imaging Sciences (A.P., R.M.B.). King's College London, United Kingdom
- School of Engineering (R.M.B.), Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Biological and Medical Engineering (R.M.B.), Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH), Santiago, Chile (M.E.A, R.M.B)
| | - Alberto Smith
- British Heart Foundation Centre of Research Excellence (P.S., C.G., J.K., L.B., A.S.P., B.M., O.L., A.P., R.M.B., A.S.), King's College London, United Kingdom
- School of Cardiovascular and Metabolic Medicine & Sciences (P.S., C.G., J.K., L.B., A.S.P., B.M., O.L., A.S.), King's College London, United Kingdom
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Chaher N, Digilio G, Lacerda S, Botnar RM, Phinikaridou A. Optimized Methods for the Surface Immobilization of Collagens and Collagen Binding Assays. J Vis Exp 2023. [PMID: 37036233 DOI: 10.3791/64720] [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] [Subscribe] [Scholar Register] [Indexed: 04/11/2023] Open
Abstract
Fibrosis occurs in various tissues as a reparative response to injury or damage. If excessive, however, fibrosis can lead to tissue scarring and organ failure, which is associated with high morbidity and mortality. Collagen is a key driver of fibrosis, with type I and type III collagen being the primary types involved in many fibrotic diseases. Unlike conventional protocols used to immobilize other proteins (e.g., elastin, albumin, fibronectin, etc.), comprehensive protocols to reproducibly immobilize different types of collagens in order to produce stable coatings are not readily available. Immobilizing collagen is surprisingly challenging because multiple experimental conditions may affect the efficiency of immobilization, including the type of collagen, the pH, the temperature, and the type of microplate used. Here, a detailed protocol to reproducibly immobilize and quantify type I and III collagens resulting in stable and reproducible gels/films is provided. Furthermore, this work demonstrates how to perform, analyze, and interpret in vitro time-resolved fluorescence binding studies to investigate the interactions between collagens and candidate collagen-binding compounds (e.g., a peptide conjugated to a metal chelate carrying, for example, europium [Eu(III)]). Such an approach can be universally applied to various biomedical applications, including the field of molecular imaging to develop targeted imaging probes, drug development, cell toxicity studies, cell proliferation studies, and immunoassays.
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Affiliation(s)
- Nadia Chaher
- School of Biomedical Engineering and Imaging Sciences, King's College London
| | - Giuseppe Digilio
- Dipartimento di Scienze e Innovazione Tecnologica, Università del Piemonte Orientale
| | - Sara Lacerda
- Centre de Biophysique Moléculaire, CNRS UPR 4301, Université d'Orléans
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London; BHF Centre of Research Excellence, Cardiovascular Division, King's College London; Escuela de Ingeniería, Pontificia Universidad Católica de Chile; Instituto de Ingeniería Biológica y Médica, Pontificia Universidad Católica de Chile
| | - Alkystis Phinikaridou
- School of Biomedical Engineering and Imaging Sciences, King's College London; BHF Centre of Research Excellence, Cardiovascular Division, King's College London;
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Lavin B, Eykyn TR, Phinikaridou A, Xavier A, Kumar S, Buqué X, Aspichueta P, Sing-Long C, Arrese M, Botnar RM, Andia ME. Characterization of hepatic fatty acids using magnetic resonance spectroscopy for the assessment of treatment response to metformin in an eNOS -/- mouse model of metabolic nonalcoholic fatty liver disease/nonalcoholic steatohepatitis. NMR Biomed 2023:e4932. [PMID: 36940044 DOI: 10.1002/nbm.4932] [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] [Received: 10/05/2022] [Revised: 03/08/2023] [Accepted: 03/09/2023] [Indexed: 06/18/2023]
Abstract
Nonalcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease worldwide. Liver biopsy remains the gold standard for diagnosis and staging of disease. There is a clinical need for noninvasive diagnostic tools for risk stratification, follow-up, and monitoring treatment response that are currently lacking, as well as preclinical models that recapitulate the etiology of the human condition. We have characterized the progression of NAFLD in eNOS-/- mice fed a high fat diet (HFD) using noninvasive Dixon-based magnetic resonance imaging and single voxel STEAM spectroscopy-based protocols to measure liver fat fraction at 3 T. After 8 weeks of diet intervention, eNOS-/- mice exhibited significant accumulation of intra-abdominal and liver fat compared with control mice. Liver fat fraction measured by 1 H-MRS in vivo showed a good correlation with the NAFLD activity score measured by histology. Treatment of HFD-fed NOS3-/- mice with metformin showed significantly reduced liver fat fraction and altered hepatic lipidomic profile compared with untreated mice. Our results show the potential of in vivo liver MRI and 1 H-MRS to noninvasively diagnose and stage the progression of NAFLD and to monitor treatment response in an eNOS-/- murine model that represents the classic NAFLD phenotype associated with metabolic syndrome.
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Affiliation(s)
- Begoña Lavin
- School of Biomedical Engineering Imaging Sciences, King's College London, London, UK
- BHF Centre of Research Excellence, Cardiovascular Division, King's College London, London, UK
- Department of Biochemistry and Molecular Biology, School of Chemistry, Complutense University, Madrid, Spain
| | - Thomas R Eykyn
- School of Biomedical Engineering Imaging Sciences, King's College London, London, UK
- BHF Centre of Research Excellence, Cardiovascular Division, King's College London, London, UK
| | - Alkystis Phinikaridou
- School of Biomedical Engineering Imaging Sciences, King's College London, London, UK
- BHF Centre of Research Excellence, Cardiovascular Division, King's College London, London, UK
| | - Aline Xavier
- Biomedical Engineering, Faculty of Engineering, Universidad de Santiago de Chile, Santiago, Chile
- ANID - Millennium Science Initiative Program - Millennium Institute Intelligent Healthcare Engineering, Santiago, Chile
| | - Shravan Kumar
- School of Biomedical Engineering Imaging Sciences, King's College London, London, UK
| | - Xabier Buqué
- Physiology Department, School of Medicine and Nursing, Universidad del País Vasco UPV/EHU, Vizcaya, Spain
- Biocruces Bizkaia Health Research Institute, Barakaldo, Spain
| | - Patricia Aspichueta
- Physiology Department, School of Medicine and Nursing, Universidad del País Vasco UPV/EHU, Vizcaya, Spain
- Biocruces Bizkaia Health Research Institute, Barakaldo, Spain
- CIBER de enfermedades hepáticas y digestivas (CIBERehd), Spain
| | - Carlos Sing-Long
- ANID - Millennium Science Initiative Program - Millennium Institute Intelligent Healthcare Engineering, Santiago, Chile
- School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Marco Arrese
- ANID - Millennium Science Initiative Program - Millennium Institute Intelligent Healthcare Engineering, Santiago, Chile
- Gastroenterology Department, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - René M Botnar
- School of Biomedical Engineering Imaging Sciences, King's College London, London, UK
- BHF Centre of Research Excellence, Cardiovascular Division, King's College London, London, UK
- ANID - Millennium Science Initiative Program - Millennium Institute Intelligent Healthcare Engineering, Santiago, Chile
- School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Marcelo E Andia
- ANID - Millennium Science Initiative Program - Millennium Institute Intelligent Healthcare Engineering, Santiago, Chile
- School of Medicine and Centro de Envejecimiento y Regeneración (CARE), Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile
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Fotaki A, Pushparajah K, Hajhosseiny R, Schneider A, Alam H, Ferreira J, Neji R, Kunze KP, Frigiola A, Botnar RM, Prieto C. Free-breathing, Contrast Agent-free Whole-Heart MTC-BOOST Imaging: Single-Center Validation Study in Adult Congenital Heart Disease. Radiol Cardiothorac Imaging 2023; 5:e220146. [PMID: 36860831 PMCID: PMC9969217 DOI: 10.1148/ryct.220146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 12/07/2022] [Accepted: 12/13/2022] [Indexed: 02/18/2023]
Abstract
Purpose To assess the clinical performance of the three-dimensional, free-breathing, Magnetization Transfer Contrast Bright-and-black blOOd phase-SensiTive (MTC-BOOST) sequence in adult congenital heart disease (ACHD). Materials and Methods In this prospective study, participants with ACHD undergoing cardiac MRI between July 2020 and March 2021 were scanned with the clinical T2-prepared balanced steady-state free precession sequence and proposed MTC-BOOST sequence. Four cardiologists scored their diagnostic confidence on a four-point Likert scale for sequential segmental analysis on images acquired with each sequence. Scan times and diagnostic confidence were compared using the Mann-Whitney test. Coaxial vascular dimensions at three anatomic landmarks were measured, and agreement between the research sequence and the corresponding clinical sequence was assessed with Bland-Altman analysis. Results The study included 120 participants (mean age, 33 years ± 13 [SD]; 65 men). The mean acquisition time of the MTC-BOOST sequence was significantly lower compared with that of the conventional clinical sequence (9 minutes ± 2 vs 14 minutes ± 5; P < .001). Diagnostic confidence was higher for the MTC-BOOST sequence compared with the clinical sequence (mean, 3.9 ± 0.3 vs 3.4 ± 0.7; P < .001). Narrow limits of agreement and mean bias less than 0.08 cm were found between the research and clinical vascular measurements. Conclusion The MTC-BOOST sequence provided efficient, high-quality, and contrast agent-free three-dimensional whole-heart imaging in ACHD, with shorter, more predictable acquisition time and improved diagnostic confidence compared with the reference standard clinical sequence.Keywords: MR Angiography, Cardiac Supplemental material is available for this article. Published under a CC BY 4.0 license.
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13
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Munoz C, Fotaki A, Botnar RM, Prieto C. Latest Advances in Image Acceleration: All Dimensions are Fair Game. J Magn Reson Imaging 2023; 57:387-402. [PMID: 36205716 PMCID: PMC10092100 DOI: 10.1002/jmri.28462] [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: 06/16/2022] [Revised: 09/20/2022] [Accepted: 09/22/2022] [Indexed: 01/20/2023] Open
Abstract
Magnetic resonance imaging (MRI) is a versatile modality that can generate high-resolution images with a variety of tissue contrasts. However, MRI is a slow technique and requires long acquisition times, which increase with higher temporal and spatial resolution and/or when multiple contrasts and large volumetric coverage is required. In order to speedup MR data acquisition, several approaches have been introduced in the literature. Most of these techniques acquire less data than required and exploit intrinsic redundancies in the MR images to recover the information that was not sampled. This article presents a review of MR acquisition and reconstruction methods that have exploited redundancies in the temporal, spatial, and contrast/parametric dimensions to accelerate image data acquisition, focusing on cardiac and abdominal MR imaging applications. The review describes how each of these dimensions has been separately exploited for speeding up MR acquisition to then discuss more advanced techniques where multiple dimensions are exploited together for further reducing scan times. Finally, future directions for multidimensional image acceleration and remaining technical challenges are discussed. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: 1.
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Affiliation(s)
- Camila Munoz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Anastasia Fotaki
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile.,Millenium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile.,Millenium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile
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Rashid I, Ginami G, Nordio G, Fotaki A, Neji R, Alam H, Pushparajah K, Frigiola A, Valverde I, Botnar RM, Prieto C. Magnetization Transfer BOOST Noncontrast Angiography Improves Pulmonary Vein Imaging in Adults With Congenital Heart Disease. J Magn Reson Imaging 2023; 57:521-531. [PMID: 35642573 PMCID: PMC10084321 DOI: 10.1002/jmri.28280] [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: 01/16/2022] [Revised: 05/14/2022] [Accepted: 05/17/2022] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Cardiac MRI plays an important role in the diagnosis and follow-up of patients with congenital heart disease (CHD). Gadolinium-based contrast agents are often needed to overcome flow-related and off-resonance artifacts that can impair the quality of conventional noncontrast 3D imaging. As serial imaging is often required in CHD, the development of robust noncontrast 3D MRI techniques is desirable. PURPOSE To assess the clinical utility of noncontrast enhanced magnetization transfer and inversion recovery prepared 3D free-breathing sequence (MTC-BOOST) compared to conventional 3D whole heart imaging in patients with CHD. STUDY TYPE Prospective, image quality. POPULATION A total of 27 adult patients (44% female, mean age 30.9 ± 14.8 years) with CHD. FIELD STRENGTH/SEQUENCE A 1.5 T; free-breathing 3D MTC-BOOST sequence. ASSESSMENT MTC-BOOST was compared to diaphragmatic navigator-gated, noncontrast T2 prepared 3D whole-heart imaging sequence (T2prep-3DWH) for comparison of vessel dimensions, lumen-to-myocardium contrast ratio (CR), and image quality (vessel wall sharpness and presence and type of artifacts) assessed by two experienced cardiologists on a 5-point scale. STATISTICAL TESTS Mann-Whitney test, paired Wilcoxon signed-rank test, Bland-Altman plots. P < 0.05 was considered statistically significant. RESULTS MTC-BOOST significantly improved image quality and CR of the right-sided pulmonary veins (PV): (CR: right upper PV 1.06 ± 0.50 vs. 0.58 ± 0.74; right lower PV 1.32 ± 0.38 vs. 0.81 ± 0.73) compared to conventional T2prep-3DWH imaging where the PVs were not visualized in some cases due to off-resonance effects. MTC-BOOST demonstrated resistance to degradation of luminal signal (assessed by CR) secondary to accelerated or turbulent flow conditions. T2prep-3DWH had higher image quality scores than MTC-BOOST for the aorta and coronary arteries; however, great vessel dimensions derived from MTC-BOOST showed excellent agreement with standard T2prep-3DWH imaging. DATA CONCLUSION MTC-BOOST allows for improved contrast-free imaging of pulmonary veins and regions characterized by accelerated or turbulent blood flow compared to standard T2prep-3DWH imaging, with excellent agreement of great vessel dimensions. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Imran Rashid
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | - Giulia Ginami
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Giovanna Nordio
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Anastasia Fotaki
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Radhouene Neji
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, UK
| | - Harith Alam
- Guy's and St Thomas' Hospital, Department of Cardiology, London, UK
| | - Kuberan Pushparajah
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Guy's and St Thomas' Hospital, Department of Cardiology, London, UK
| | | | - Israel Valverde
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Paediatric Cardiology Unit, Hospital Virgen del Rocio and Institute of Biomedicine of Seville, IBIS Ciber-CV, Seville, Spain
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
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15
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Fotaki A, Velasco C, Prieto C, Botnar RM. Quantitative MRI in cardiometabolic disease: From conventional cardiac and liver tissue mapping techniques to multi-parametric approaches. Front Cardiovasc Med 2023; 9:991383. [PMID: 36756640 PMCID: PMC9899858 DOI: 10.3389/fcvm.2022.991383] [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] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 12/29/2022] [Indexed: 01/24/2023] Open
Abstract
Cardiometabolic disease refers to the spectrum of chronic conditions that include diabetes, hypertension, atheromatosis, non-alcoholic fatty liver disease, and their long-term impact on cardiovascular health. Histological studies have confirmed several modifications at the tissue level in cardiometabolic disease. Recently, quantitative MR methods have enabled non-invasive myocardial and liver tissue characterization. MR relaxation mapping techniques such as T1, T1ρ, T2 and T2* provide a pixel-by-pixel representation of the corresponding tissue specific relaxation times, which have been shown to correlate with fibrosis, altered tissue perfusion, oedema and iron levels. Proton density fat fraction mapping approaches allow measurement of lipid tissue in the organ of interest. Several studies have demonstrated their utility as early diagnostic biomarkers and their potential to bear prognostic implications. Conventionally, the quantification of these parameters by MRI relies on the acquisition of sequential scans, encoding and mapping only one parameter per scan. However, this methodology is time inefficient and suffers from the confounding effects of the relaxation parameters in each single map, limiting wider clinical and research applications. To address these limitations, several novel approaches have been proposed that encode multiple tissue parameters simultaneously, providing co-registered multiparametric information of the tissues of interest. This review aims to describe the multi-faceted myocardial and hepatic tissue alterations in cardiometabolic disease and to motivate the application of relaxometry and proton-density cardiac and liver tissue mapping techniques. Current approaches in myocardial and liver tissue characterization as well as latest technical developments in multiparametric quantitative MRI are included. Limitations and challenges of these novel approaches, and recommendations to facilitate clinical validation are also discussed.
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Affiliation(s)
- Anastasia Fotaki
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom,*Correspondence: Anastasia Fotaki,
| | - Carlos Velasco
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom,School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile,Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile,Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
| | - René M. Botnar
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom,School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile,Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile,Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
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16
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Phair A, Cruz G, Qi H, Botnar RM, Prieto C. Free-running 3D whole-heart T 1 and T 2 mapping and cine MRI using low-rank reconstruction with non-rigid cardiac motion correction. Magn Reson Med 2023; 89:217-232. [PMID: 36198014 PMCID: PMC9828568 DOI: 10.1002/mrm.29449] [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] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 07/14/2022] [Accepted: 08/18/2022] [Indexed: 01/12/2023]
Abstract
PURPOSE To introduce non-rigid cardiac motion correction into a novel free-running framework for the simultaneous acquisition of 3D whole-heart myocardial <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> maps and cine images, enabling a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>∼</mml:mo></mml:mrow> <mml:annotation>$$ \sim $$</mml:annotation></mml:semantics> </mml:math> 3-min scan. METHODS Data were acquired using a free-running 3D golden-angle radial readout interleaved with inversion recovery and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> -preparation pulses. After correction for translational respiratory motion, non-rigid cardiac-motion-corrected reconstruction with dictionary-based low-rank compression and patch-based regularization enabled 3D whole-heart <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> mapping at any given cardiac phase as well as whole-heart cardiac cine imaging. The framework was validated and compared with established methods in 11 healthy subjects. RESULTS Good quality 3D <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> maps and cine images were reconstructed for all subjects. Septal <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> values using the proposed approach ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>1200</mml:mn> <mml:mo>±</mml:mo> <mml:mn>50</mml:mn></mml:mrow> <mml:annotation>$$ 1200\pm 50 $$</mml:annotation></mml:semantics> </mml:math> ms) were higher than those from a 2D MOLLI sequence ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>1063</mml:mn> <mml:mo>±</mml:mo> <mml:mn>33</mml:mn></mml:mrow> <mml:annotation>$$ 1063\pm 33 $$</mml:annotation></mml:semantics> </mml:math> ms), which is known to underestimate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> , while <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> values from the proposed approach ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>51</mml:mn> <mml:mo>±</mml:mo> <mml:mn>4</mml:mn></mml:mrow> <mml:annotation>$$ 51\pm 4 $$</mml:annotation></mml:semantics> </mml:math> ms) were in good agreement with those from a 2D GraSE sequence ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>51</mml:mn> <mml:mo>±</mml:mo> <mml:mn>2</mml:mn></mml:mrow> <mml:annotation>$$ 51\pm 2 $$</mml:annotation></mml:semantics> </mml:math> ms). CONCLUSION The proposed technique provides 3D <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> maps and cine images with isotropic spatial resolution in a single <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>∼</mml:mo></mml:mrow> <mml:annotation>$$ \sim $$</mml:annotation></mml:semantics> </mml:math> 3.3-min scan.
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Affiliation(s)
- Andrew Phair
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Gastão Cruz
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Haikun Qi
- School of Biomedical EngineeringShanghaiTech UniversityShanghaiChina
| | - René M. Botnar
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK,Instituto de Ingeniería Biológica y MédicaPontificia Universidad Católica de ChileSantiagoChile,Escuela de IngenieríaPontificia Universidad Católica de ChileSantiagoChile,Millennium Institute for Intelligent Healthcare EngineeringSantiagoChile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK,Escuela de IngenieríaPontificia Universidad Católica de ChileSantiagoChile,Millennium Institute for Intelligent Healthcare EngineeringSantiagoChile
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17
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Fujita S, Sano K, Cruz G, Fukumura Y, Kawasaki H, Fukunaga I, Morita Y, Yoneyama M, Kamagata K, Abe O, Ikejima K, Botnar RM, Prieto C, Aoki S. MR Fingerprinting for Liver Tissue Characterization: A Histopathologic Correlation Study. Radiology 2023; 306:150-159. [PMID: 36040337 DOI: 10.1148/radiol.220736] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Liver MR fingerprinting (MRF) enables simultaneous quantification of T1, T2, T2*, and proton density fat fraction (PDFF) maps in single breath-hold acquisitions. Histopathologic correlation studies are desired for its clinical use. Purpose To compare liver MRF-derived metrics with separate reference quantitative MRI in participants with diffuse liver disease, evaluate scan-rescan repeatability of liver MRF, and validate MRF-derived measurements for histologic grading of liver biopsies. Materials and Methods This prospective study included participants with diffuse liver disease undergoing MRI from July 2021 to January 2022. Participants underwent two-dimensional single-section liver MRF and separate reference quantitative MRI. Linear regression, Bland-Altman plots, and coefficients of variation were used to assess the bias and repeatability of liver MRF measurements. For participants undergoing liver biopsy, the association between mapping and histologic grading was evaluated by using the Spearman correlation coefficient. Results Fifty-six participants (mean age, 59 years ± 15 [SD]; 32 women) were included to compare mapping techniques and 23 participants were evaluated with liver biopsy (mean age, 52.7 years ± 12.7; 14 women). The linearity of MRF with reference measurements in participants with diffuse liver disease (R2 value) for T1, T2, T2*, and PDFF maps was 0.86, 0.88, 0.54, and 0.99, respectively. The overall coefficients of variation for repeatability in the liver were 3.2%, 5.5%, 7.1%, and 4.6% for T1, T2, T2*, and PDFF maps, respectively. MRF-derived metrics showed high diagnostic performance in differentiating moderate or severe changes from mild or no changes (area under the receiver operating characteristic curve for fibrosis, inflammation, steatosis, and siderosis: 0.62 [95% CI: 0.52, 0.62], 0.92 [95% CI: 0.88, 0.92], 0.97 [95% CI: 0.96, 0.97], and 0.74 [95% CI: 0.57, 0.74], respectively). Conclusion Liver MR fingerprinting provided repeatable T1, T2, T2*, and proton density fat fraction maps in high agreement with reference quantitative mapping and may correlate with pathologic grades in participants with diffuse liver disease. © RSNA, 2022 Online supplemental material is available for this article.
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Affiliation(s)
- Shohei Fujita
- From the Departments of Radiology (S.F., K.S., H.K., I.F., Y.M., K.K., S.A.), Human Pathology (Y.F.), and Gastroenterology (K.I.), Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo, Tokyo 113-8421, Japan; Department of Radiology, University of Tokyo, Tokyo, Japan (S.F., Y.M., O.A.); Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom (G.C., R.M.B., C.P.); Department of MR Clinical Science, Philips Japan, Tokyo, Japan (M.Y.); School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
| | - Katsuhiro Sano
- From the Departments of Radiology (S.F., K.S., H.K., I.F., Y.M., K.K., S.A.), Human Pathology (Y.F.), and Gastroenterology (K.I.), Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo, Tokyo 113-8421, Japan; Department of Radiology, University of Tokyo, Tokyo, Japan (S.F., Y.M., O.A.); Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom (G.C., R.M.B., C.P.); Department of MR Clinical Science, Philips Japan, Tokyo, Japan (M.Y.); School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
| | - Gastao Cruz
- From the Departments of Radiology (S.F., K.S., H.K., I.F., Y.M., K.K., S.A.), Human Pathology (Y.F.), and Gastroenterology (K.I.), Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo, Tokyo 113-8421, Japan; Department of Radiology, University of Tokyo, Tokyo, Japan (S.F., Y.M., O.A.); Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom (G.C., R.M.B., C.P.); Department of MR Clinical Science, Philips Japan, Tokyo, Japan (M.Y.); School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
| | - Yuki Fukumura
- From the Departments of Radiology (S.F., K.S., H.K., I.F., Y.M., K.K., S.A.), Human Pathology (Y.F.), and Gastroenterology (K.I.), Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo, Tokyo 113-8421, Japan; Department of Radiology, University of Tokyo, Tokyo, Japan (S.F., Y.M., O.A.); Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom (G.C., R.M.B., C.P.); Department of MR Clinical Science, Philips Japan, Tokyo, Japan (M.Y.); School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
| | - Hideo Kawasaki
- From the Departments of Radiology (S.F., K.S., H.K., I.F., Y.M., K.K., S.A.), Human Pathology (Y.F.), and Gastroenterology (K.I.), Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo, Tokyo 113-8421, Japan; Department of Radiology, University of Tokyo, Tokyo, Japan (S.F., Y.M., O.A.); Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom (G.C., R.M.B., C.P.); Department of MR Clinical Science, Philips Japan, Tokyo, Japan (M.Y.); School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
| | - Issei Fukunaga
- From the Departments of Radiology (S.F., K.S., H.K., I.F., Y.M., K.K., S.A.), Human Pathology (Y.F.), and Gastroenterology (K.I.), Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo, Tokyo 113-8421, Japan; Department of Radiology, University of Tokyo, Tokyo, Japan (S.F., Y.M., O.A.); Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom (G.C., R.M.B., C.P.); Department of MR Clinical Science, Philips Japan, Tokyo, Japan (M.Y.); School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
| | - Yuichi Morita
- From the Departments of Radiology (S.F., K.S., H.K., I.F., Y.M., K.K., S.A.), Human Pathology (Y.F.), and Gastroenterology (K.I.), Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo, Tokyo 113-8421, Japan; Department of Radiology, University of Tokyo, Tokyo, Japan (S.F., Y.M., O.A.); Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom (G.C., R.M.B., C.P.); Department of MR Clinical Science, Philips Japan, Tokyo, Japan (M.Y.); School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
| | - Masami Yoneyama
- From the Departments of Radiology (S.F., K.S., H.K., I.F., Y.M., K.K., S.A.), Human Pathology (Y.F.), and Gastroenterology (K.I.), Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo, Tokyo 113-8421, Japan; Department of Radiology, University of Tokyo, Tokyo, Japan (S.F., Y.M., O.A.); Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom (G.C., R.M.B., C.P.); Department of MR Clinical Science, Philips Japan, Tokyo, Japan (M.Y.); School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
| | - Koji Kamagata
- From the Departments of Radiology (S.F., K.S., H.K., I.F., Y.M., K.K., S.A.), Human Pathology (Y.F.), and Gastroenterology (K.I.), Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo, Tokyo 113-8421, Japan; Department of Radiology, University of Tokyo, Tokyo, Japan (S.F., Y.M., O.A.); Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom (G.C., R.M.B., C.P.); Department of MR Clinical Science, Philips Japan, Tokyo, Japan (M.Y.); School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
| | - Osamu Abe
- From the Departments of Radiology (S.F., K.S., H.K., I.F., Y.M., K.K., S.A.), Human Pathology (Y.F.), and Gastroenterology (K.I.), Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo, Tokyo 113-8421, Japan; Department of Radiology, University of Tokyo, Tokyo, Japan (S.F., Y.M., O.A.); Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom (G.C., R.M.B., C.P.); Department of MR Clinical Science, Philips Japan, Tokyo, Japan (M.Y.); School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
| | - Kenichi Ikejima
- From the Departments of Radiology (S.F., K.S., H.K., I.F., Y.M., K.K., S.A.), Human Pathology (Y.F.), and Gastroenterology (K.I.), Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo, Tokyo 113-8421, Japan; Department of Radiology, University of Tokyo, Tokyo, Japan (S.F., Y.M., O.A.); Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom (G.C., R.M.B., C.P.); Department of MR Clinical Science, Philips Japan, Tokyo, Japan (M.Y.); School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
| | - René M Botnar
- From the Departments of Radiology (S.F., K.S., H.K., I.F., Y.M., K.K., S.A.), Human Pathology (Y.F.), and Gastroenterology (K.I.), Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo, Tokyo 113-8421, Japan; Department of Radiology, University of Tokyo, Tokyo, Japan (S.F., Y.M., O.A.); Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom (G.C., R.M.B., C.P.); Department of MR Clinical Science, Philips Japan, Tokyo, Japan (M.Y.); School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
| | - Claudia Prieto
- From the Departments of Radiology (S.F., K.S., H.K., I.F., Y.M., K.K., S.A.), Human Pathology (Y.F.), and Gastroenterology (K.I.), Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo, Tokyo 113-8421, Japan; Department of Radiology, University of Tokyo, Tokyo, Japan (S.F., Y.M., O.A.); Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom (G.C., R.M.B., C.P.); Department of MR Clinical Science, Philips Japan, Tokyo, Japan (M.Y.); School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
| | - Shigeki Aoki
- From the Departments of Radiology (S.F., K.S., H.K., I.F., Y.M., K.K., S.A.), Human Pathology (Y.F.), and Gastroenterology (K.I.), Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo, Tokyo 113-8421, Japan; Department of Radiology, University of Tokyo, Tokyo, Japan (S.F., Y.M., O.A.); Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom (G.C., R.M.B., C.P.); Department of MR Clinical Science, Philips Japan, Tokyo, Japan (M.Y.); School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
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18
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Abstract
Myocardial inflammation occurs following activation of the cardiac immune system, producing characteristic changes in the myocardial tissue. Cardiovascular magnetic resonance is the non-invasive imaging gold standard for myocardial tissue characterization, and is able to detect image signal changes that may occur resulting from inflammation, including edema, hyperemia, capillary leak, necrosis, and fibrosis. Conventional cardiovascular magnetic resonance for the detection of myocardial inflammation and its sequela include T2-weighted imaging, parametric T1- and T2-mapping, and gadolinium-based contrast-enhanced imaging. Emerging techniques seek to image several parameters simultaneously for myocardial tissue characterization, and to depict subtle immune-mediated changes, such as immune cell activity in the myocardium and cardiac cell metabolism. This review article outlines the underlying principles of current and emerging cardiovascular magnetic resonance methods for imaging myocardial inflammation.
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Affiliation(s)
- Katharine E Thomas
- University of Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom (K.E.T., V.M.F.)
| | - Anastasia Fotaki
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, United Kingdom (A.F., R.M.B.)
| | - René M Botnar
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, United Kingdom (A.F., R.M.B.)
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B.)
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile (R.M.B.)
| | - Vanessa M Ferreira
- University of Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom (K.E.T., V.M.F.)
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19
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Fotaki A, Fuin N, Nordio G, Velasco Jimeno C, Qi H, Emmanuel Y, Pushparajah K, Botnar RM, Prieto C. Accelerating 3D MTC-BOOST in patients with congenital heart disease using a joint multi-scale variational neural network reconstruction. Magn Reson Imaging 2022; 92:120-132. [PMID: 35772584 PMCID: PMC9826869 DOI: 10.1016/j.mri.2022.06.012] [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] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 06/12/2022] [Accepted: 06/23/2022] [Indexed: 01/13/2023]
Abstract
PURPOSE Free-breathing Magnetization Transfer Contrast Bright blOOd phase SensiTive (MTC-BOOST) is a prototype balanced-Steady-State Free Precession sequence for 3D whole-heart imaging, that employs the endogenous magnetisation transfer contrast mechanism. This achieves reduction of flow and off-resonance artefacts, that often arise with the clinical T2prepared balanced-Steady-State Free Precession sequence, enabling high quality, contrast-agent free imaging of the thoracic cardiovascular anatomy. Fully-sampled MTC-BOOST acquisition requires long scan times (~10-24 min) and therefore acceleration is needed to permit its clinical incorporation. The aim of this study is to enable and clinically validate the 5-fold accelerated MTC-BOOST acquisition with joint Multi-Scale Variational Neural Network (jMS-VNN) reconstruction. METHODS Thirty-six patients underwent free-breathing, 3D whole-heart imaging with the MTC-BOOST sequence, which is combined with variable density spiral-like Cartesian sampling and 2D image navigators for translational motion estimation. This sequence acquires two differently weighted bright-blood volumes in an interleaved fashion, which are then joined in a phase sensitive inversion recovery reconstruction to obtain a complementary fully co-registered black-blood volume. Data from eighteen patients were used for training, whereas data from the remaining eighteen patients were used for testing/evaluation. The proposed deep-learning based approach adopts a supervised multi-scale variational neural network for joint reconstruction of the two differently weighted bright-blood volumes acquired with the 5-fold accelerated MTC-BOOST. The two contrast images are stacked as different channels in the network to exploit the shared information. The proposed approach is compared to the fully-sampled MTC-BOOST and 5-fold undersampled MTC-BOOST acquisition with Compressed Sensing (CS) reconstruction in terms of scan/reconstruction time and bright-blood image quality. Comparison against conventional 2-fold undersampled T2-prepared 3D bright-blood whole-heart clinical sequence (T2prep-3DWH) is also included. RESULTS Acquisition time was 3.0 ± 1.0 min for the 5-fold accelerated MTC-BOOST versus 9.0 ± 1.1 min for the fully-sampled MTC-BOOST and 11.1 ± 2.6 min for the T2prep-3DWH (p < 0.001 and p < 0.001, respectively). Reconstruction time was significantly lower with the jMS-VNN method compared to CS (10 ± 0.5 min vs 20 ± 2 s, p < 0.001). Image quality was higher for the proposed 5-fold undersampled jMS-VNN versus conventional CS, comparable or higher to the corresponding T2prep-3DWH dataset and similar to the fully-sampled MTC-BOOST. CONCLUSION The proposed 5-fold accelerated jMS-VNN MTC-BOOST framework provides efficient 3D whole-heart bright-blood imaging in fast acquisition and reconstruction time with concomitant reduction of flow and off-resonance artefacts, that are frequently encountered with the clinical sequence. Image quality of the cardiac anatomy and thoracic vasculature is comparable or superior to the clinical scan and 5-fold CS reconstruction in faster reconstruction time, promising potential clinical adoption.
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Affiliation(s)
- Anastasia Fotaki
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | - Niccolo Fuin
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Giovanna Nordio
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Carlos Velasco Jimeno
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Haikun Qi
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Yaso Emmanuel
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Kuberan Pushparajah
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - René M Botnar
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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20
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Velasco C, Fletcher TJ, Botnar RM, Prieto C. Artificial intelligence in cardiac magnetic resonance fingerprinting. Front Cardiovasc Med 2022; 9:1009131. [PMID: 36204566 PMCID: PMC9530662 DOI: 10.3389/fcvm.2022.1009131] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 08/30/2022] [Indexed: 11/13/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a fast MRI-based technique that allows for multiparametric quantitative characterization of the tissues of interest in a single acquisition. In particular, it has gained attention in the field of cardiac imaging due to its ability to provide simultaneous and co-registered myocardial T1 and T2 mapping in a single breath-held cardiac MRF scan, in addition to other parameters. Initial results in small healthy subject groups and clinical studies have demonstrated the feasibility and potential of MRF imaging. Ongoing research is being conducted to improve the accuracy, efficiency, and robustness of cardiac MRF. However, these improvements usually increase the complexity of image reconstruction and dictionary generation and introduce the need for sequence optimization. Each of these steps increase the computational demand and processing time of MRF. The latest advances in artificial intelligence (AI), including progress in deep learning and the development of neural networks for MRI, now present an opportunity to efficiently address these issues. Artificial intelligence can be used to optimize candidate sequences and reduce the memory demand and computational time required for reconstruction and post-processing. Recently, proposed machine learning-based approaches have been shown to reduce dictionary generation and reconstruction times by several orders of magnitude. Such applications of AI should help to remove these bottlenecks and speed up cardiac MRF, improving its practical utility and allowing for its potential inclusion in clinical routine. This review aims to summarize the latest developments in artificial intelligence applied to cardiac MRF. Particularly, we focus on the application of machine learning at different steps of the MRF process, such as sequence optimization, dictionary generation and image reconstruction.
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Affiliation(s)
- Carlos Velasco
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- *Correspondence: Carlos Velasco
| | - Thomas J. Fletcher
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - René M. Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
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21
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Dorfman AL, Geva T, Samyn MM, Greil G, Krishnamurthy R, Messroghli D, Festa P, Secinaro A, Soriano B, Taylor A, Taylor MD, Botnar RM, Lai WW. SCMR expert consensus statement for cardiovascular magnetic resonance of acquired and non-structural pediatric heart disease. J Cardiovasc Magn Reson 2022; 24:44. [PMID: 35864534 PMCID: PMC9302232 DOI: 10.1186/s12968-022-00873-1] [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] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 06/24/2022] [Indexed: 12/14/2022] Open
Abstract
Cardiovascular magnetic resonance (CMR) is widely used for diagnostic imaging in the pediatric population. In addition to structural congenital heart disease (CHD), for which published guidelines are available, CMR is also performed for non-structural pediatric heart disease, for which guidelines are not available. This article provides guidelines for the performance and reporting of CMR in the pediatric population for non-structural ("non-congenital") heart disease, including cardiomyopathies, myocarditis, Kawasaki disease and systemic vasculitides, cardiac tumors, pericardial disease, pulmonary hypertension, heart transplant, and aortopathies. Given important differences in disease pathophysiology and clinical manifestations as well as unique technical challenges related to body size, heart rate, and sedation needs, these guidelines focus on optimization of the CMR examination in infants and children compared to adults. Disease states are discussed, including the goals of CMR examination, disease-specific protocols, and limitations and pitfalls, as well as newer techniques that remain under development.
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Affiliation(s)
- Adam L. Dorfman
- Department of Pediatrics, Division of Pediatric Cardiology, University of Michigan C.S. Mott Children’s Hospital, 1540 E. Medical Center Drive, Ann Arbor, MI 48109 USA
| | - Tal Geva
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Ave, Boston, MA 02115 USA
| | - Margaret M. Samyn
- Department of Pediatrics, Division of Pediatric Cardiology, Medical College of Wisconsin/Herma Heart Institute, Children’s Wisconsin, Milwaukee, WI 53226 USA
| | - Gerald Greil
- Department of Pediatrics, Division of Pediatric Cardiology, University of Texas Southwestern Medical Center, Dallas, TX 75235 USA
| | - Rajesh Krishnamurthy
- Department of Radiology, Nationwide Children’s Hospital, 700 Children’s Dr. E4A, Columbus, OH 43205 USA
| | - Daniel Messroghli
- Department of Internal Medicine-Cardiology, Deutsches Herzzentrum Berlin and Charité-University Medicine Berlin, Berlin, Germany
| | - Pierluigi Festa
- Department of Cardiology, Fondazione Toscana G. Monasterio, Massa, Italy
| | - Aurelio Secinaro
- Advanced Cardiothoracic Imaging Unit, Department of Imaging, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
| | - Brian Soriano
- Department of Pediatrics, Division of Pediatric Cardiology, Seattle Children’s Hospital, 4800 Sand Point Way NE, Seattle, WA 98105 USA
| | - Andrew Taylor
- Department of Cardiovascular Imaging, Great Ormond Street Hospital for Sick Children, University College London, London, UK
| | - Michael D. Taylor
- Department of Pediatrics, Division of Pediatric Cardiology, Cincinnati Children’s Hospital, 3333 Burnet Ave #2129, Cincinnati, OH 45229 USA
| | - René M. Botnar
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Wyman W. Lai
- CHOC Children’s, 1201 W. La Veta Avenue, Orange, CA 92868 USA
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22
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Holtackers RJ, Emrich T, Botnar RM, Kooi ME, Wildberger JE, Kreitner KF. Late Gadolinium Enhancement Cardiac Magnetic Resonance Imaging: From Basic Concepts to Emerging Methods. ROFO-FORTSCHR RONTG 2022; 194:491-504. [PMID: 35196714 DOI: 10.1055/a-1718-4355] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
BACKGROUND Late gadolinium enhancement (LGE) is a widely used cardiac magnetic resonance imaging (MRI) technique to diagnose a broad range of ischemic and non-ischemic cardiomyopathies. Since its development and validation against histology already more than two decades ago, the clinical utility of LGE and its span of applications have increased considerably. METHODS In this review we will present the basic concepts of LGE imaging and its diagnostic and prognostic value, elaborate on recent developments and emerging methods, and finally discuss future prospects. RESULTS Continuous developments in 3 D imaging methods, motion correction techniques, water/fat-separated imaging, dark-blood methods, and scar quantification improved the performance and further expanded the clinical utility of LGE imaging. CONCLUSION LGE imaging is the current noninvasive reference standard for the assessment of myocardial viability. Improvements in spatial resolution, scar-to-blood contrast, and water/fat-separated imaging further strengthened its position. KEY POINTS · LGE MRI is the reference standard for the noninvasive assessment of myocardial viability. · LGE MRI is used to diagnose a broad range of non-ischemic cardiomyopathies in everyday clinical practice.. · Improvements in spatial resolution and scar-to-blood contrast further strengthened its position. · Continuous developments improve its performance and further expand its clinical utility. CITATION FORMAT · Holtackers RJ, Emrich T, Botnar RM et al. Late Gadolinium Enhancement Cardiac Magnetic Resonance Imaging: From Basic Concepts to Emerging Methods. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1718-4355.
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Affiliation(s)
- Robert J Holtackers
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, the Netherlands.,Department of Radiology & Nuclear Medicine, Maastricht University Medical Centre, the Netherlands.,School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom
| | - Tilman Emrich
- Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Rhine Main, Mainz, Germany.,Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - René M Botnar
- School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.,Pontificia Universidad Católica de Chile, Escuela de Ingeniería, Santiago, Chile
| | - M Eline Kooi
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, the Netherlands.,Department of Radiology & Nuclear Medicine, Maastricht University Medical Centre, the Netherlands
| | - Joachim E Wildberger
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, the Netherlands.,Department of Radiology & Nuclear Medicine, Maastricht University Medical Centre, the Netherlands
| | - K-F Kreitner
- Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Germany
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23
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Fotaki A, Munoz C, Emanuel Y, Hua A, Bosio F, Kunze KP, Neji R, Masci PG, Botnar RM, Prieto C. Efficient non-contrast enhanced 3D Cartesian cardiovascular magnetic resonance angiography of the thoracic aorta in 3 min. J Cardiovasc Magn Reson 2022; 24:5. [PMID: 35000609 PMCID: PMC8744314 DOI: 10.1186/s12968-021-00839-9] [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] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 12/15/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The application of cardiovascular magnetic resonance angiography (CMRA) for the assessment of thoracic aortic disease is often associated with prolonged and unpredictable acquisition times and residual motion artefacts. To overcome these limitations, we have integrated undersampled acquisition with image-based navigators and inline non-rigid motion correction to enable a free-breathing, contrast-free Cartesian CMRA framework for the visualization of the thoracic aorta in a short and predictable scan of 3 min. METHODS 35 patients with thoracic aortic disease (36 ± 13y, 14 female) were prospectively enrolled in this single-center study. The proposed 3D T2-prepared balanced steady state free precession (bSSFP) sequence with image-based navigator (iNAV) was compared to the clinical 3D T2-prepared bSSFP with diaphragmatic-navigator gating (dNAV), in terms of image acquisition time. Three cardiologists blinded to iNAV vs. dNAV acquisition, recorded image quality scores across four aortic segments and their overall diagnostic confidence. Contrast ratio (CR) and relative standard deviation (RSD) of signal intensity (SI) in the corresponding segments were estimated. Co-axial aortic dimensions in six landmarks were measured by two readers to evaluate the agreement between the two methods, along with inter-observer and intra-observer agreement. Kolmogorov-Smirnov test, Mann-Whitney U (MWU), Bland-Altman analysis (BAA), intraclass correlation coefficient (ICC) were used for statistical analysis. RESULTS The scan time for the iNAV-based approach was significantly shorter (3.1 ± 0.5 min vs. 12.0 ± 3.0 min for dNAV, P = 0.005). Reconstruction was performed inline in 3.0 ± 0.3 min. Diagnostic confidence was similar for the proposed iNAV versus dNAV for all three reviewers (Reviewer 1: 3.9 ± 0.3 vs. 3.8 ± 0.4, P = 0.7; Reviewer 2: 4.0 ± 0.2 vs. 3.9 ± 0.3, P = 0.4; Reviewer 3: 3.8 ± 0.4 vs. 3.7 ± 0.6, P = 0.3). The proposed method yielded higher image quality scores in terms of artefacts from respiratory motion, and non-diagnostic images due to signal inhomogeneity were observed less frequently. While the dNAV approach outperformed the iNAV method in the CR assessment, the iNAV sequence showed improved signal homogeneity along the entire thoracic aorta [RSD SI 5.1 (4.4, 6.5) vs. 6.5 (4.6, 8.6), P = 0.002]. BAA showed a mean difference of < 0.05 cm across the 6 landmarks between the two datasets. ICC showed excellent inter- and intra-observer reproducibility. CONCLUSIONS Thoracic aortic iNAV-based CMRA with fast acquisition (~ 3 min) and inline reconstruction (3 min) is proposed, resulting in high diagnostic confidence and reproducible aortic measurements.
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Affiliation(s)
- Anastasia Fotaki
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor-Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK.
| | - Camila Munoz
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor-Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
| | - Yaso Emanuel
- Department of Cardiology, NHS Foundation Trust, Guy's and St Thomas, London, UK
| | - Alina Hua
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor-Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
| | - Filippo Bosio
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor-Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
| | - Karl P Kunze
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor-Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, UK
| | - Radhouene Neji
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor-Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, UK
| | - Pier Giorgio Masci
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor-Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
- Department of Cardiology, NHS Foundation Trust, Guy's and St Thomas, London, UK
| | - René M Botnar
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor-Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor-Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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24
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Schneider A, Cruz G, Munoz C, Hajhosseiny R, Kuestner T, Kunze KP, Neji R, Botnar RM, Prieto C. Whole-heart non-rigid motion corrected coronary MRA with autofocus virtual 3D iNAV. Magn Reson Imaging 2022; 87:169-176. [PMID: 34999163 DOI: 10.1016/j.mri.2022.01.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [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: 12/01/2021] [Accepted: 01/04/2022] [Indexed: 01/21/2023]
Abstract
PURPOSE Respiratory motion-corrected coronary MR angiography (CMRA) has shown promise for assessing coronary disease. By incorporating coronal 2D image navigators (iNAVs), respiratory motion can be corrected for in a beat-to-beat basis using translational correction in the foot-head (FH) and right-left (RL) directions and in a bin-to-bin basis using non-rigid motion correction addressing the remaining FH, RL and anterior-posterior (AP) motion. However, with this approach beat-to-beat AP motion is not corrected for. In this work we investigate the effect of remaining beat-to-beat AP motion and propose a virtual 3D iNAV that exploits autofocus motion correction to enable beat-to-beat AP and improved RL intra-bin motion correction. METHODS Free-breathing 3D whole-heart CMRA was acquired using a 3-fold undersampled variable-density Cartesian trajectory. Beat-to-beat 3D translational respiratory motion was estimated from the 2D iNAVs in FH and RL directions, and in AP direction with autofocus assuming a linear relationship between FH and AP movement of the heart. Furthermore, motion in RL was also refined using autofocus. This virtual 3D (v3D) iNAV was incorporated in a non-rigid motion correction (NRMC) framework. The proposed approach was tested in 12 cardiac patients, and visible vessel length and vessel sharpness for the right (RCA) and left (LAD) coronary arteries were compared against 2D iNAV-based NRMC. RESULTS Average vessel sharpness and length in v3D iNAV NRMC was improved compared to 2D iNAV NRMC (vessel sharpness: RCA: 56 ± 1% vs 52 ± 11%, LAD: 49 ± 8% vs 49 ± 7%; visible vessel length: RCA: 5.98 ± 1.37 cm vs 5.81 ± 1.62 cm, LAD: 5.95 ± 1.85 cm vs 4.83 ± 1.56 cm), however these improvements were not statistically significant. CONCLUSION The proposed virtual 3D iNAV NRMC reconstruction further improved NRMC CMRA image quality by reducing artefacts arising from residual AP motion, however the level of improvement was subject-dependent.
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Affiliation(s)
- Alina Schneider
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | - Gastao Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Camila Munoz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Reza Hajhosseiny
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Thomas Kuestner
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Medical Image and Data Analysis, Department of Interventional and Diagnostic Radiology, University Hospital of Tübingen, Tübingen, Germany
| | - Karl P Kunze
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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25
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Munoz C, Qi H, Cruz G, Küstner T, Botnar RM, Prieto C. Self-supervised learning-based diffeomorphic non-rigid motion estimation for fast motion-compensated coronary MR angiography. Magn Reson Imaging 2022; 85:10-18. [PMID: 34655727 DOI: 10.1016/j.mri.2021.10.004] [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] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 10/01/2021] [Accepted: 10/10/2021] [Indexed: 11/17/2022]
Abstract
PURPOSE To accelerate non-rigid motion corrected coronary MR angiography (CMRA) reconstruction by developing a deep learning based non-rigid motion estimation network and combining this with an efficient implementation of the undersampled motion corrected reconstruction. METHODS Undersampled and respiratory motion corrected CMRA with overall short scans of 5 to 10 min have been recently proposed. However, image reconstruction with this approach remains lengthy, since it relies on several non-rigid image registrations to estimate the respiratory motion and on a subsequent iterative optimization to correct for motion during the undersampled reconstruction. Here we introduce a self-supervised diffeomorphic non-rigid respiratory motion estimation network, DiRespME-net, to speed up respiratory motion estimation. We couple this with an efficient GPU-based implementation of the subsequent motion-corrected iterative reconstruction. DiRespME-net is based on a U-Net architecture, and is trained in a self-supervised fashion, with a loss enforcing image similarity and spatial smoothness of the motion fields. Motion predicted by DiRespME-net was used for GPU-based motion-corrected CMRA in 12 test subjects and final images were compared to those produced by state-of-the-art reconstruction. Vessel sharpness and visible length of the right coronary artery (RCA) and the left anterior descending (LAD) coronary artery were used as metrics of image quality for comparison. RESULTS No statistically significant difference in image quality was found between images reconstructed with the proposed approach (MC:DiRespME-net) and a motion-corrected reconstruction using cubic B-splines (MC:Nifty-reg). Visible vessel length was not significantly different between methods (RCA: MC:Nifty-reg 5.7 ± 1.7 cm vs MC:DiRespME-net 5.8 ± 1.7 cm, P = 0.32; LAD: MC:Nifty-reg 7.0 ± 2.6 cm vs MC:DiRespME-net 6.9 ± 2.7 cm, P = 0.81). Similarly, no statistically significant difference between methods was observed in terms of vessel sharpness (RCA: MC:Nifty-reg 60.3 ± 7.2% vs MC:DiRespME-net 61.0 ± 6.8%, P = 0.19; LAD: MC:Nifty-reg 57.4 ± 7.9% vs MC:DiRespME-net 58.1 ± 7.5%, P = 0.27). The proposed approach achieved a 50-fold reduction in computation time, resulting in a total reconstruction time of approximately 20 s. CONCLUSIONS The proposed self-supervised learning-based motion corrected reconstruction enables fast motion-corrected CMRA image reconstruction, holding promise for integration in clinical routine.
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Affiliation(s)
- Camila Munoz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | - Haikun Qi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Gastao Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Thomas Küstner
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Medical Image and Data Analysis, Department of Interventional and Diagnostic Radiology, University Hospital of Tübingen, Tübingen, Germany
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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Lavin B, Andia ME, Saha P, Botnar RM, Phinikaridou A. Quantitative MRI of Endothelial Permeability and (Dys)function in Atherosclerosis. J Vis Exp 2021. [PMID: 34978293 DOI: 10.3791/62724] [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] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
Abstract
Cardiovascular diseases are the leading causes of death worldwide. A permeable/leaky and dysfunctional endothelium is considered the earliest marker of vascular damage and thought to drive atherosclerosis. A method to identify these changes in vivo would be desirable in the clinic. Magnetic resonance imaging (MRI)-based tools and other technologies have enabled a profound understanding of the role of the endothelium in cardiovascular diseases and risk in vivo. There is, however, a need for reproducible and simple approaches for extracting quantifiable data reflective of endothelial damage from a single imaging study. A non-invasive, easy-to-implement, and quantitative MRI workflow was developed to acquire and analyze images that allow the quantification of two imaging biomarkers of arterial endothelial damage (leakiness/permeability and dysfunction). Here, the protocol describes the application of this method in the brachiocephalic artery of atherosclerotic ApoE-/- mice using a clinical MRI scanner. First, late gadolinium enhancement (LGE) and Modified Look-Locker Inversion Recovery (MOLLI) T1 mapping protocols to quantify endothelial leakage using an albumin-binding probe are described. Second, anatomic, and quantitative blood flow sequences to measure endothelial dysfunction, in response to acetylcholine are described. Importantly, the method outlined here allows the acquisition of high-spatial-resolution 3D images with large volumetric coverage enabling accurate segmentation of vessel wall structures to improve inter- and intra-observer variability and to increase reliability and reproducibility. Additionally, it provides quantitative data without the need for high-temporal resolution for complex kinetic modeling, making it model-independent and even allowing for imaging of highly mobile vessels (coronary arteries). Therefore, the approach simplifies and expedites data analysis. Finally, this method can be implemented on different scanners, can be extended to image different arterial beds, and is clinically applicable for use in humans. This method could be used to diagnose and treat patients with atherosclerosis by adopting a precision-medicine approach.
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Affiliation(s)
- Begoña Lavin
- School of Biomedical Engineering and Imaging Sciences, King's College London; BHF Centre of Excellence, Cardiovascular Division, King's College London; Biochemistry and Molecular Biology Department, School of Chemistry, Complutense University
| | - Marcelo E Andia
- School of Biomedical Engineering and Imaging Sciences, King's College London; Radiology Department, School of Medicine, Pontificia Universidad Católica de Chile; ANID - Millennium Science Initiative Program - Millennium Nucleus for Cardiovascular Magnetic Resonance
| | - Prakash Saha
- Academic Department of Vascular Surgery, Cardiovascular Division, King's College London
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London; BHF Centre of Excellence, Cardiovascular Division, King's College London; Wellcome Trust and EPSRC Medical Engineering Center, King's College London; Escuela de Ingeniería, Universidad Católica de Chile
| | - Alkystis Phinikaridou
- School of Biomedical Engineering and Imaging Sciences, King's College London; BHF Centre of Excellence, Cardiovascular Division, King's College London;
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Velasco C, Cruz G, Lavin B, Hua A, Fotaki A, Botnar RM, Prieto C. Simultaneous T 1 , T 2 , and T 1ρ cardiac magnetic resonance fingerprinting for contrast agent-free myocardial tissue characterization. Magn Reson Med 2021; 87:1992-2002. [PMID: 34799854 DOI: 10.1002/mrm.29091] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [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: 04/08/2021] [Revised: 10/28/2021] [Accepted: 11/01/2021] [Indexed: 12/15/2022]
Abstract
PURPOSE To develop a simultaneous T1 , T2 , and T1ρ cardiac magnetic resonance fingerprinting (MRF) approach to enable comprehensive contrast agent-free myocardial tissue characterization in a single breath-hold scan. METHODS A 2D gradient-echo electrocardiogram-triggered cardiac MRF sequence with low flip angles, varying magnetization preparation, and spiral trajectory was acquired at 1.5 T to encode T1 , T2 , and T1⍴ simultaneously. The MRF images were reconstructed using low-rank inversion, regularized with a multicontrast patch-based higher-order reconstruction. Parametric maps were generated and matched in the singular value domain to extended phase graph-based dictionaries. The proposed approach was tested in phantoms and 10 healthy subjects and compared against conventional methods in terms of coefficients of determination and best fits for the phantom study, and in terms of Bland-Altman agreement, average values and coefficient of variation of T1 , T2 , and T1⍴ for the healthy subjects study. RESULTS The T1 , T2 , and T1⍴ MRF values showed excellent correlation with conventional spin-echo and clinical mapping methods in phantom studies (r2 > 0.97). Measured MRF values in myocardial tissue (mean ± SD) were 1133 ± 33 ms, 38.8 ± 3.5 ms, and 52.0 ± 4.0 ms for T1 , T2 and T1⍴ , respectively, against 1053 ± 47 ms, 50.4 ± 3.9 ms, and 55.9 ± 3.3 ms for T1 modified Look-Locker inversion imaging, T2 gradient and spin echo, and T1⍴ turbo field echo, respectively. CONCLUSION A cardiac MRF approach for simultaneous quantification of myocardial T1 , T2 , and T1ρ in a single breath-hold MR scan of about 16 seconds has been proposed. The approach has been investigated in phantoms and healthy subjects showing good agreement with reference spin echo measurements and conventional clinical maps.
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Affiliation(s)
- Carlos Velasco
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Gastão Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Begoña Lavin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Biochemistry and Molecular Biology, School of Chemistry, Complutense University of Madrid, Madrid, Spain
| | - Alina Hua
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Anastasia Fotaki
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
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Velasco C, Cruz G, Jaubert O, Lavin B, Botnar RM, Prieto C. Simultaneous comprehensive liver T 1 , T 2 , T 2 ∗ , T 1ρ , and fat fraction characterization with MR fingerprinting. Magn Reson Med 2021; 87:1980-1991. [PMID: 34792212 DOI: 10.1002/mrm.29089] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [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: 06/02/2021] [Revised: 10/18/2021] [Accepted: 10/29/2021] [Indexed: 12/23/2022]
Abstract
PURPOSE To develop a novel simultaneous co-registered T1 , T2 , T 2 ∗ , T1ρ , and fat fraction abdominal MR fingerprinting (MRF) approach for fully comprehensive liver-tissue characterization in a single breath-hold scan. METHODS A gradient-echo liver MRF sequence with low fixed flip angle, multi-echo radial readout, and varying magnetization preparation pulses for multiparametric encoding is performed at 1.5 T. The T 2 ∗ and fat fraction are estimated from a graph/cut water/fat separation method using a six-peak fat model. Water/fat singular images obtained are then matched to an MRF dictionary, estimating water-specific T1 , T2 , and T1ρ . The proposed approach was tested in phantoms and 10 healthy subjects and compared against conventional sequences. RESULTS For the phantom studies, linear fits show excellent coefficients of determination (r2 > 0.9) for every parametric map. For in vivo studies, the average values measured within regions of interest drawn on liver, spleen, muscle, and fat are statistically different from the reference scans (p < 0.05) for T1 , T2 , and T1⍴ but not for T 2 ∗ and fat fraction, whereas correlation between MRF and reference scans is excellent for each parameter (r2 > 0.92 for every parameter). CONCLUSION The proposed multi-echo inversion-recovery, T2 , and T1⍴ prepared liver MRF sequence presented in this work allows for quantitative T1 , T2 , T 2 ∗ , T1⍴ , and fat fraction liver-tissue characterization in a single breath-hold scan of 18 seconds. The approach showed good agreement and correlation with respect to reference clinical maps.
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Affiliation(s)
- Carlos Velasco
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Gastão Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Olivier Jaubert
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Begoña Lavin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Biochemistry and Molecular Biology, School of Chemistry, Complutense University of Madrid, Madrid, Spain
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
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Capuana F, Phinikaridou A, Stefania R, Padovan S, Lavin B, Lacerda S, Almouazen E, Chevalier Y, Heinrich-Balard L, Botnar RM, Aime S, Digilio G. Imaging of Dysfunctional Elastogenesis in Atherosclerosis Using an Improved Gadolinium-Based Tetrameric MRI Probe Targeted to Tropoelastin. J Med Chem 2021; 64:15250-15261. [PMID: 34661390 PMCID: PMC8558862 DOI: 10.1021/acs.jmedchem.1c01286] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Dysfunctional elastin turnover plays a major role in the progression of atherosclerotic plaques. Failure of tropoelastin cross-linking into mature elastin leads to the accumulation of tropoelastin within the growing plaque, increasing its instability. Here we present Gd4-TESMA, an MRI contrast agent specifically designed for molecular imaging of tropoelastin within plaques. Gd4-TESMA is a tetrameric probe composed of a tropoelastin-binding peptide (the VVGS-peptide) conjugated with four Gd(III)-DOTA-monoamide chelates. It shows a relaxivity per molecule of 34.0 ± 0.8 mM-1 s-1 (20 MHz, 298 K, pH 7.2), a good binding affinity to tropoelastin (KD = 41 ± 12 μM), and a serum half-life longer than 2 h. Gd4-TESMA accumulates specifically in atherosclerotic plaques in the ApoE-/- murine model of plaque progression, with 2 h persistence of contrast enhancement. As compared to the monomeric counterpart (Gd-TESMA), the tetrameric Gd4-TESMA probe shows a clear advantage regarding both sensitivity and imaging time window, allowing for a better characterization of atherosclerotic plaques.
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Affiliation(s)
- Federico Capuana
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, Turin 10126, Italy
| | - Alkystis Phinikaridou
- School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London SE1 7EH, United Kingdom
| | - Rachele Stefania
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, Turin 10126, Italy
| | - Sergio Padovan
- Institute for Biostructures and Bioimages (CNR) c/o Molecular Biotechnology Center, Via Nizza 52, Torino 10126, Italy
| | - Begoña Lavin
- School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London SE1 7EH, United Kingdom.,Department of Biochemistry and Molecular Biology, School of Chemistry, Complutense University, Ciudad Universitaria s/n, Madrid 28040, Spain
| | - Sara Lacerda
- Centre de Biophysique Moléculaire, CNRS, UPR 4301, Université d'Orléans, Rue Charles Sadron, Orléans Cedex 2 45071, France
| | - Eyad Almouazen
- CNRS, LAGEPP UMR 5007, Univ Lyon, Université Claude Bernard Lyon 1, 43 boulevard du 11 novembre 1918, Villeurbanne 69622, France
| | - Yves Chevalier
- CNRS, LAGEPP UMR 5007, Univ Lyon, Université Claude Bernard Lyon 1, 43 boulevard du 11 novembre 1918, Villeurbanne 69622, France
| | - Laurence Heinrich-Balard
- INSA Lyon, CNRS, MATEIS, UMR5510, Univ Lyon, Université Claude Bernard Lyon 1, Villeurbanne 69100, France
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London SE1 7EH, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Avda. Vicuña Mackenna, Santiago 4860, Chile
| | | | - Giuseppe Digilio
- Department of Science and Technologic Innovation, Università del Piemonte Orientale ″Amedeo Avogadro″, Viale T. Michel 11, Alessandria 15121, Italy
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Hajhosseiny R, Munoz C, Cruz G, Khamis R, Kim WY, Prieto C, Botnar RM. Coronary Magnetic Resonance Angiography in Chronic Coronary Syndromes. Front Cardiovasc Med 2021; 8:682924. [PMID: 34485397 PMCID: PMC8416045 DOI: 10.3389/fcvm.2021.682924] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 03/19/2021] [Accepted: 07/23/2021] [Indexed: 01/14/2023] Open
Abstract
Cardiovascular disease is the leading cause of mortality worldwide, with atherosclerotic coronary artery disease (CAD) accounting for the majority of cases. X-ray coronary angiography and computed tomography coronary angiography (CCTA) are the imaging modalities of choice for the assessment of CAD. However, the use of ionising radiation and iodinated contrast agents remain drawbacks. There is therefore a clinical need for an alternative modality for the early identification and longitudinal monitoring of CAD without these associated drawbacks. Coronary magnetic resonance angiography (CMRA) could be a potential alternative for the detection and monitoring of coronary arterial stenosis, without exposing patients to ionising radiation or iodinated contrast agents. Further advantages include its versatility, excellent soft tissue characterisation and suitability for repeat imaging. Despite the early promise of CMRA, widespread clinical utilisation remains limited due to long and unpredictable scan times, onerous scan planning, lower spatial resolution, as well as motion related image quality degradation. The past decade has brought about a resurgence in CMRA technology, with significant leaps in image acceleration, respiratory and cardiac motion estimation and advanced motion corrected or motion-resolved image reconstruction. With the advent of artificial intelligence, great advances are also seen in deep learning-based motion estimation, undersampled and super-resolution reconstruction promising further improvements of CMRA. This has enabled high spatial resolution (1 mm isotropic), 3D whole heart CMRA in a clinically feasible and reliable acquisition time of under 10 min. Furthermore, latest super-resolution image reconstruction approaches which are currently under evaluation promise acquisitions as short as 1 min. In this review, we will explore the recent technological advances that are designed to bring CMRA closer to clinical reality.
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Affiliation(s)
- Reza Hajhosseiny
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Camila Munoz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Gastao Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Ramzi Khamis
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Won Yong Kim
- Department of Cardiology and Institute of Clinical Medicine, Aarhus University Hospital, Skejby, Denmark
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - René M. Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Instituto de Ingeniería Biologica y Medica, Pontificia Universidad Catolica de Chile, Santiago, Chile
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Holtackers RJ, Van De Heyning CM, Chiribiri A, Wildberger JE, Botnar RM, Kooi ME. Dark-blood late gadolinium enhancement cardiovascular magnetic resonance for improved detection of subendocardial scar: a review of current techniques. J Cardiovasc Magn Reson 2021; 23:96. [PMID: 34289866 PMCID: PMC8296731 DOI: 10.1186/s12968-021-00777-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 05/17/2021] [Indexed: 12/02/2022] Open
Abstract
For almost 20 years, late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been the reference standard for the non-invasive assessment of myocardial viability. Since the blood pool often appears equally bright as the enhanced scar regions, detection of subendocardial scar patterns can be challenging. Various novel LGE methods have been proposed that null or suppress the blood signal by employing additional magnetization preparation mechanisms. This review aims to provide a comprehensive overview of these dark-blood LGE methods, discussing the magnetization preparation schemes and findings in phantom, preclinical, and clinical studies. Finally, conclusions on the current evidence and limitations are drawn and new avenues for future research are discussed. Dark-blood LGE methods are a promising new tool for non-invasive assessment of myocardial viability. For a mainstream adoption of dark-blood LGE, however, clinical availability and ease of use are crucial.
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Affiliation(s)
- Robert J. Holtackers
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, PO Box 616, Maastricht, 6200 MD The Netherlands
- Department of Radiology & Nuclear Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | | | - Amedeo Chiribiri
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Joachim E. Wildberger
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, PO Box 616, Maastricht, 6200 MD The Netherlands
- Department of Radiology & Nuclear Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - René M. Botnar
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - M. Eline Kooi
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, PO Box 616, Maastricht, 6200 MD The Netherlands
- Department of Radiology & Nuclear Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
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32
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Milotta G, Munoz C, Kunze KP, Neji R, Figliozzi S, Chiribiri A, Hajhosseiny R, Masci PG, Prieto C, Botnar RM. 3D whole-heart grey-blood late gadolinium enhancement cardiovascular magnetic resonance imaging. J Cardiovasc Magn Reson 2021; 23:62. [PMID: 34024276 PMCID: PMC8142497 DOI: 10.1186/s12968-021-00751-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [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: 06/04/2020] [Accepted: 03/29/2021] [Indexed: 12/30/2022] Open
Abstract
PURPOSE To develop a free-breathing whole-heart isotropic-resolution 3D late gadolinium enhancement (LGE) sequence with Dixon-encoding, which provides co-registered 3D grey-blood phase-sensitive inversion-recovery (PSIR) and complementary 3D fat volumes in a single scan of < 7 min. METHODS A free-breathing 3D PSIR LGE sequence with dual-echo Dixon readout with a variable density Cartesian trajectory with acceleration factor of 3 is proposed. Image navigators are acquired to correct both inversion recovery (IR)-prepared and reference volumes for 2D translational respiratory motion, enabling motion compensated PSIR reconstruction with 100% respiratory scan efficiency. An intermediate PSIR reconstruction is performed between the in-phase echoes to estimate the signal polarity which is subsequently applied to the IR-prepared water volume to generate a water grey-blood PSIR image. The IR-prepared water volume is obtained using a water/fat separation algorithm from the corresponding dual-echo readout. The complementary fat-volume is obtained after water/fat separation of the reference volume. Ten patients (6 with myocardial scar) were scanned with the proposed water/fat grey-blood 3D PSIR LGE sequence at 1.5 T and compared to breath-held grey-blood 2D LGE sequence in terms of contrast ratio (CR), contrast-to-noise ratio (CNR), scar depiction, scar transmurality, scar mass and image quality. RESULTS Comparable CRs (p = 0.98, 0.40 and 0.83) and CNRs (p = 0.29, 0.40 and 0.26) for blood-myocardium, scar-myocardium and scar-blood respectively were obtained with the proposed free-breathing 3D water/fat LGE and 2D clinical LGE scan. Excellent agreement for scar detection, scar transmurality, scar mass (bias = 0.29%) and image quality scores (from 1: non-diagnostic to 4: excellent) of 3.8 ± 0.42 and 3.6 ± 0.69 (p > 0.99) were obtained with the 2D and 3D PSIR LGE approaches with comparable total acquisition time (p = 0.29). Similar agreement in intra and inter-observer variability were obtained for the 2D and 3D acquisition respectively. CONCLUSION The proposed approach enabled the acquisition of free-breathing motion-compensated isotropic-resolution 3D grey-blood PSIR LGE and fat volumes. The proposed approach showed good agreement with conventional 2D LGE in terms of CR, scar depiction and scan time, while enabling free-breathing acquisition, whole-heart coverage, reformatting in arbitrary views and visualization of both water and fat information.
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Affiliation(s)
- Giorgia Milotta
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital (3rd Floor - Lambeth Wing), Westminster Bridge Road, London, SE1 7EH, UK.
| | - Camila Munoz
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital (3rd Floor - Lambeth Wing), Westminster Bridge Road, London, SE1 7EH, UK
| | - Karl P Kunze
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital (3rd Floor - Lambeth Wing), Westminster Bridge Road, London, SE1 7EH, UK
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, UK
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital (3rd Floor - Lambeth Wing), Westminster Bridge Road, London, SE1 7EH, UK
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, UK
| | - Stefano Figliozzi
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital (3rd Floor - Lambeth Wing), Westminster Bridge Road, London, SE1 7EH, UK
| | - Amedeo Chiribiri
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital (3rd Floor - Lambeth Wing), Westminster Bridge Road, London, SE1 7EH, UK
| | - Reza Hajhosseiny
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital (3rd Floor - Lambeth Wing), Westminster Bridge Road, London, SE1 7EH, UK
| | - Pier Giorgio Masci
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital (3rd Floor - Lambeth Wing), Westminster Bridge Road, London, SE1 7EH, UK
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital (3rd Floor - Lambeth Wing), Westminster Bridge Road, London, SE1 7EH, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital (3rd Floor - Lambeth Wing), Westminster Bridge Road, London, SE1 7EH, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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Hajhosseiny R, Rashid I, Bustin A, Munoz C, Cruz G, Nazir MS, Grigoryan K, Ismail TF, Preston R, Neji R, Kunze K, Razavi R, Chiribiri A, Masci PG, Rajani R, Prieto C, Botnar RM. Clinical comparison of sub-mm high-resolution non-contrast coronary CMR angiography against coronary CT angiography in patients with low-intermediate risk of coronary artery disease: a single center trial. J Cardiovasc Magn Reson 2021; 23:57. [PMID: 33993890 PMCID: PMC8127202 DOI: 10.1186/s12968-021-00758-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [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: 11/28/2020] [Accepted: 04/06/2021] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND The widespread clinical application of coronary cardiovascular magnetic resonance (CMR) angiography (CMRA) for the assessment of coronary artery disease (CAD) remains limited due to low scan efficiency leading to prolonged and unpredictable acquisition times; low spatial-resolution; and residual respiratory motion artefacts resulting in limited image quality. To overcome these limitations, we have integrated highly undersampled acquisitions with image-based navigators and non-rigid motion correction to enable high resolution (sub-1 mm3) free-breathing, contrast-free 3D whole-heart coronary CMRA with 100% respiratory scan efficiency in a clinically feasible and predictable acquisition time. OBJECTIVES To evaluate the diagnostic performance of this coronary CMRA framework against coronary computed tomography angiography (CTA) in patients with suspected CAD. METHODS Consecutive patients (n = 50) with suspected CAD were examined on a 1.5T CMR scanner. We compared the diagnostic accuracy of coronary CMRA against coronary CTA for detecting a ≥ 50% reduction in luminal diameter. RESULTS The 50 recruited patients (55 ± 9 years, 33 male) completed coronary CMRA in 10.7 ± 1.4 min. Twelve (24%) had significant CAD on coronary CTA. Coronary CMRA obtained diagnostic image quality in 95% of all, 97% of proximal, 97% of middle and 90% of distal coronary segments. The sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy were: per patient (100%, 74%, 55%, 100% and 80%), per vessel (81%, 88%, 46%, 97% and 88%) and per segment (76%, 95%, 44%, 99% and 94%) respectively. CONCLUSIONS The high diagnostic image quality and diagnostic performance of coronary CMRA compared against coronary CTA demonstrates the potential of coronary CMRA as a robust and safe non-invasive alternative for excluding significant disease in patients at low-intermediate risk of CAD.
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Affiliation(s)
- Reza Hajhosseiny
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rdfloor Lambeth Wing, London, SE1 7EH, UK.
| | - Imran Rashid
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rdfloor Lambeth Wing, London, SE1 7EH, UK
| | - Aurélien Bustin
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rdfloor Lambeth Wing, London, SE1 7EH, UK
| | - Camila Munoz
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rdfloor Lambeth Wing, London, SE1 7EH, UK
| | - Gastao Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rdfloor Lambeth Wing, London, SE1 7EH, UK
| | - Muhummad Sohaib Nazir
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rdfloor Lambeth Wing, London, SE1 7EH, UK
- Department of Cardiology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Karine Grigoryan
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rdfloor Lambeth Wing, London, SE1 7EH, UK
- Department of Cardiology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Tevfik F Ismail
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rdfloor Lambeth Wing, London, SE1 7EH, UK
- Department of Cardiology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Rebecca Preston
- Department of Radiology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rdfloor Lambeth Wing, London, SE1 7EH, UK
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, UK
| | - Karl Kunze
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rdfloor Lambeth Wing, London, SE1 7EH, UK
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, UK
| | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rdfloor Lambeth Wing, London, SE1 7EH, UK
| | - Amedeo Chiribiri
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rdfloor Lambeth Wing, London, SE1 7EH, UK
| | - Pier Giorgio Masci
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rdfloor Lambeth Wing, London, SE1 7EH, UK
| | - Ronak Rajani
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rdfloor Lambeth Wing, London, SE1 7EH, UK
- Department of Cardiology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rdfloor Lambeth Wing, London, SE1 7EH, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rdfloor Lambeth Wing, London, SE1 7EH, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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Bustin A, Hua A, Milotta G, Jaubert O, Hajhosseiny R, Ismail TF, Botnar RM, Prieto C. High-Spatial-Resolution 3D Whole-Heart MRI T2 Mapping for Assessment of Myocarditis. Radiology 2021; 298:578-586. [PMID: 33464179 PMCID: PMC7924517 DOI: 10.1148/radiol.2021201630] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [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: 04/16/2020] [Revised: 09/25/2020] [Accepted: 10/07/2020] [Indexed: 12/20/2022]
Abstract
Background Clinical guidelines recommend the use of established T2 mapping sequences to detect and quantify myocarditis and edema, but T2 mapping is performed in two dimensions with limited coverage and repetitive breath holds. Purpose To assess the reproducibility of an accelerated free-breathing three-dimensional (3D) whole-heart T2 MRI mapping sequence in phantoms and participants without a history of cardiac disease and to investigate its clinical performance in participants with suspected myocarditis. Materials and Methods Eight participants (three women, mean age, 31 years ± 4 [standard deviation]; cohort 1) without a history of cardiac disease and 25 participants (nine women, mean age, 45 years ± 17; cohort 2) with clinically suspected myocarditis underwent accelerated free-breathing 3D whole-heart T2 mapping with 100% respiratory scanning efficiency at 1.5 T. The participants were enrolled from November 2018 to August 2020. Three repeated scans were performed on 2 separate days in cohort 1. Segmental variations in T2 relaxation times of the left ventricular myocardium were assessed, and intrasession and intersession reproducibility were measured. In cohort 2, segmental myocardial T2 values, detection of focal inflammation, and map quality were compared with those obtained from clinical breath-hold two-dimensional (2D) T2 mapping. Statistical differences were assessed using the nonparametric Mann-Whitney and Kruskal-Wallis tests, whereas the paired Wilcoxon signed-rank test was used to assess subjective scores. Results Whole-heart T2 maps were acquired in a mean time of 6 minutes 53 seconds ± 1 minute 5 seconds at 1.5 mm3 resolution. Breath-hold 2D and free-breathing 3D T2 mapping had similar intrasession (mean T2 change of 3.2% and 2.3% for 2D and 3D, respectively) and intersession (4.8% and 4.9%, respectively) reproducibility. The two T2 mapping sequences showed similar map quality (P = .23, cohort 2). Abnormal myocardial segments were identified with confidence (score 3) in 14 of 25 participants (56%) with 3D T2 mapping and only in 10 of 25 participants (40%) with 2D T2 mapping. Conclusion High-spatial-resolution three-dimensional (3D) whole-heart T2 mapping shows high intrasession and intersession reproducibility and helps provide T2 myocardial characterization in agreement with clinical two-dimensional reference, while enabling 3D assessment of focal disease with higher confidence. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Friedrich in this issue.
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Affiliation(s)
- Aurélien Bustin
- From the Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, 3rd Floor, Lambeth Wing, St Thomas’ Hospital, London SE1 7EH, England (A.B., A.H., G.M., O.J., R.H., T.F.I., R.M.B., C.P.); and Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
| | - Alina Hua
- From the Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, 3rd Floor, Lambeth Wing, St Thomas’ Hospital, London SE1 7EH, England (A.B., A.H., G.M., O.J., R.H., T.F.I., R.M.B., C.P.); and Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
| | - Giorgia Milotta
- From the Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, 3rd Floor, Lambeth Wing, St Thomas’ Hospital, London SE1 7EH, England (A.B., A.H., G.M., O.J., R.H., T.F.I., R.M.B., C.P.); and Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
| | - Olivier Jaubert
- From the Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, 3rd Floor, Lambeth Wing, St Thomas’ Hospital, London SE1 7EH, England (A.B., A.H., G.M., O.J., R.H., T.F.I., R.M.B., C.P.); and Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
| | - Reza Hajhosseiny
- From the Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, 3rd Floor, Lambeth Wing, St Thomas’ Hospital, London SE1 7EH, England (A.B., A.H., G.M., O.J., R.H., T.F.I., R.M.B., C.P.); and Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
| | - Tevfik F. Ismail
- From the Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, 3rd Floor, Lambeth Wing, St Thomas’ Hospital, London SE1 7EH, England (A.B., A.H., G.M., O.J., R.H., T.F.I., R.M.B., C.P.); and Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
| | - René M. Botnar
- From the Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, 3rd Floor, Lambeth Wing, St Thomas’ Hospital, London SE1 7EH, England (A.B., A.H., G.M., O.J., R.H., T.F.I., R.M.B., C.P.); and Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
| | - Claudia Prieto
- From the Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, 3rd Floor, Lambeth Wing, St Thomas’ Hospital, London SE1 7EH, England (A.B., A.H., G.M., O.J., R.H., T.F.I., R.M.B., C.P.); and Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile (R.M.B., C.P.)
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Aarntzen E, Achilefu S, Akam EA, Albaghdadi M, Beer AJ, Bharti S, Bhujwalla ZM, Bischof GN, Biswal S, Boss M, Botnar RM, Brinson Z, Brom M, Buitinga M, Bulte JW, Caravan P, Chan HP, Chandy M, Chaney AM, Chen DL, Chen X(S, Chenevert TL, Coughlin JM, Covington MF, Cumming P, Daldrup-Link HE, Deal EM, de Galan B, Derlin T, Dewhirst MW, Di Paolo A, Drzezga A, Du Y, Thi-Quynh Duong M, Ehman RL, Eriksson O, Galli F, Gatenby RA, Gelovani J, Giehl K, Giger ML, Goel R, Gold G, Gotthardt M, Graham MM, Gropler RJ, Gründer G, Gulhane A, Hadjiiski L, Hajhosseiny R, Hammoud DA, Helfer BM, Hicks RJ, Higuchi T, Hoffman JM, Honer M, Huang SC(H, Hung J, Hwang DW, Jackson IM, Jacobs AH, Jaffer FA, Jain SK, James ML, Jansen T, Johansson L, Joosten L, Kakkad S, Kamson D, Kang SR, Kelly KA, Knopp MI, Knopp MV, Kogan F, Krishnamachary B, Künnecke B, Lee DS, Libby P, Luker GD, Luker KE, Makowski MR, Mankoff DA, Massoud TF, Meyer CR, Miller Z, Min JJ, Mondal SB, Montesi SB, Navin PJ, Nekolla SG, Niu G, Notohamiprodjo S, Ordoñez AA, Osborn EA, Pacheco-Torres J, Pagano G, Palmer GM, Paulmurugan R, Penet MF, Phinikaridou A, Pomper MG, Prieto C, Qi H, Raghunand N, Ramar T, Reynolds F, Ropella-Panagis K, Ross BD, Rowe SP, Rudin M, Sadaghiani MS, Sager H, Samala R, Saraste A, Schelhaas S, Schwaiger M, Schwarz SW, Seiberlich N, Shapiro MG, Shim H, Signore A, Solnes LB, Suh M, Tsien C, van Eimeren T, Varasteh Z, Venkatesh SK, Viel T, Waerzeggers Y, Wahl RL, Weber W, Werner RA, Winkeler A, Wong DF, Wright CL, Wu AM, Wu JC, Yoon D, You SH, Yuan C, Yuan H, Zanzonico P, Zhao XQ, Zhou IY, Zinnhardt B. Contributors. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.01004-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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Hajhosseiny R, Prieto C, Qi H, Phinikaridou A, Botnar RM. Thrombosis and Embolism. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00072-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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Correia T, Ginami G, Rashid I, Nordio G, Hajhosseiny R, Ismail TF, Neji R, Botnar RM, Prieto C. Accelerated high-resolution free-breathing 3D whole-heart T 2-prepared black-blood and bright-blood cardiovascular magnetic resonance. J Cardiovasc Magn Reson 2020; 22:88. [PMID: 33317570 PMCID: PMC7737390 DOI: 10.1186/s12968-020-00691-3] [Citation(s) in RCA: 4] [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: 05/22/2020] [Accepted: 11/18/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The free-breathing 3D whole-heart T2-prepared Bright-blood and black-blOOd phase SensiTive inversion recovery (BOOST) cardiovascular magnetic resonance (CMR) sequence was recently proposed for simultaneous bright-blood coronary CMR angiography and black-blood late gadolinium enhancement (LGE) imaging. This sequence enables simultaneous visualization of cardiac anatomy, coronary arteries and fibrosis. However, high-resolution (< 1.4 × 1.4 × 1.4 mm3) fully-sampled BOOST requires long acquisition times of ~ 20 min. METHODS In this work, we propose to extend a highly efficient respiratory-resolved motion-corrected reconstruction framework (XD-ORCCA) to T2-prepared BOOST to enable high-resolution 3D whole-heart coronary CMR angiography and black-blood LGE in a clinically feasible scan time. Twelve healthy subjects were imaged without contrast injection (pre-contrast BOOST) and 10 patients with suspected cardiovascular disease were imaged after contrast injection (post-contrast BOOST). A quantitative analysis software was used to compare accelerated pre-contrast BOOST against the fully-sampled counterpart (vessel sharpness and length of the left and right coronary arteries). Moreover, three cardiologists performed diagnostic image quality scoring for clinical 2D LGE and both bright- and black-blood 3D BOOST imaging using a 4-point scale (1-4, non-diagnostic-fully diagnostic). A two one-sided test of equivalence (TOST) was performed to compare the pre-contrast BOOST images. Nonparametric TOST was performed to compare post-contrast BOOST image quality scores. RESULTS The proposed method produces images from 3.8 × accelerated non-contrast-enhanced BOOST acquisitions with comparable vessel length and sharpness to those obtained from fully- sampled scans in healthy subjects. Moreover, in terms of visual grading, the 3D BOOST LGE datasets (median 4) and the clinical 2D counterpart (median 3.5) were found to be statistically equivalent (p < 0.05). In addition, bright-blood BOOST images allowed for visualization of the proximal and middle left anterior descending and right coronary sections with high diagnostic quality (mean score > 3.5). CONCLUSIONS The proposed framework provides high-resolution 3D whole-heart BOOST images from a single free-breathing acquisition in ~ 7 min.
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Affiliation(s)
- Teresa Correia
- School of Biomedical Engineering and Imaging Sciences, King’s College London, Lambeth Wing, St Thomas’ Hospital, London, UK
| | - Giulia Ginami
- School of Biomedical Engineering and Imaging Sciences, King’s College London, Lambeth Wing, St Thomas’ Hospital, London, UK
| | - Imran Rashid
- School of Biomedical Engineering and Imaging Sciences, King’s College London, Lambeth Wing, St Thomas’ Hospital, London, UK
| | - Giovanna Nordio
- School of Biomedical Engineering and Imaging Sciences, King’s College London, Lambeth Wing, St Thomas’ Hospital, London, UK
| | - Reza Hajhosseiny
- School of Biomedical Engineering and Imaging Sciences, King’s College London, Lambeth Wing, St Thomas’ Hospital, London, UK
| | - Tevfik F. Ismail
- School of Biomedical Engineering and Imaging Sciences, King’s College London, Lambeth Wing, St Thomas’ Hospital, London, UK
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King’s College London, Lambeth Wing, St Thomas’ Hospital, London, UK
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, UK
| | - René M. Botnar
- School of Biomedical Engineering and Imaging Sciences, King’s College London, Lambeth Wing, St Thomas’ Hospital, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King’s College London, Lambeth Wing, St Thomas’ Hospital, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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Hajhosseiny R, Bustin A, Munoz C, Rashid I, Cruz G, Manning WJ, Prieto C, Botnar RM. Coronary Magnetic Resonance Angiography: Technical Innovations Leading Us to the Promised Land? JACC Cardiovasc Imaging 2020; 13:2653-2672. [PMID: 32199836 DOI: 10.1016/j.jcmg.2020.01.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [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: 05/24/2019] [Revised: 01/03/2020] [Accepted: 01/08/2020] [Indexed: 02/07/2023]
Abstract
Coronary artery disease remains the leading cause of cardiovascular morbidity and mortality. Invasive X-ray angiography and coronary computed tomography angiography are established gold standards for coronary luminography. However, they expose patients to invasive complications, ionizing radiation, and iodinated contrast agents. Among a number of imaging modalities, coronary cardiovascular magnetic resonance (CMR) angiography may be used in some cases as an alternative for the detection and monitoring of coronary arterial stenosis, with advantages including its versatility, excellent soft tissue characterization, and avoidance of ionizing radiation and iodinated contrast agents. In this review, we explore the recent advances in motion correction, image acceleration, and reconstruction technologies that are bringing coronary CMR angiography closer to widespread clinical implementation.
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Affiliation(s)
- Reza Hajhosseiny
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; National Heart and Lung Institute, Imperial College London, London, United Kingdom.
| | - Aurelien Bustin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Camila Munoz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Imran Rashid
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Gastao Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Warren J Manning
- Department of Medicine (Cardiovascular Division) and Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Escuela de Ingeniería, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Escuela de Ingeniería, Pontificia Universidad Catolica de Chile, Santiago, Chile
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López K, Neji R, Bustin A, Rashid I, Hajhosseiny R, Malik SJ, Teixeira RPAG, Razavi R, Prieto C, Roujol S, Botnar RM. Quantitative magnetization transfer imaging for non-contrast enhanced detection of myocardial fibrosis. Magn Reson Med 2020; 85:2069-2083. [PMID: 33201524 DOI: 10.1002/mrm.28577] [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: 02/06/2020] [Revised: 09/10/2020] [Accepted: 10/09/2020] [Indexed: 11/09/2022]
Abstract
PURPOSE To develop a novel gadolinium-free model-based quantitative magnetization transfer (qMT) technique to assess macromolecular changes associated with myocardial fibrosis. METHODS The proposed sequence consists of a two-dimensional breath-held dual shot interleaved acquisition of five MT-weighted (MTw) spoiled gradient echo images, with variable MT flip angles (FAs) and off-resonance frequencies. A two-pool exchange model and dictionary matching were used to quantify the pool size ratio (PSR) and bound pool T2 relaxation ( T 2 B ). The signal model was developed and validated using 25 MTw images on a bovine serum albumin (BSA) phantom and in vivo human thigh muscle. A protocol with five MTw images was optimized for single breath-hold cardiac qMT imaging. The proposed sequence was tested in 10 healthy subjects and 5 patients with myocardial fibrosis and compared to late gadolinium enhancement (LGE). RESULTS PSR values in the BSA phantom were within the confidence interval of previously reported values (concentration 10% BSA = 5.9 ± 0.1%, 15% BSA = 9.4 ± 0.2%). PSR and T 2 B in thigh muscle were also in agreement with literature (PSR = 10.9 ± 0.3%, T 2 B = 6.4 ± 0.4 us). In 10 healthy subjects, global left ventricular PSR was 4.30 ± 0.65%. In patients, PSR was reduced in areas associated with LGE (remote: 4.68 ± 0.70% vs. fibrotic: 3.12 ± 0.78 %, n = 5, P < .002). CONCLUSION In vivo model-based qMT mapping of the heart was performed for the first time, with promising results for non-contrast enhanced assessment of myocardial fibrosis.
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Affiliation(s)
- Karina López
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,MR Research Collaboration, Siemens Healthcare Limited, Frimley, UK
| | - Aurelien Bustin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Imran Rashid
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Reza Hajhosseiny
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Shaihan J Malik
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Rui Pedro A G Teixeira
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Sébastien Roujol
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
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Lavin B, Lacerda S, Andia ME, Lorrio S, Bakewell R, Smith A, Rashid I, Botnar RM, Phinikaridou A. Tropoelastin: an in vivo imaging marker of dysfunctional matrix turnover during abdominal aortic dilation. Cardiovasc Res 2020; 116:995-1005. [PMID: 31282949 PMCID: PMC7104357 DOI: 10.1093/cvr/cvz178] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 07/05/2019] [Indexed: 12/15/2022] Open
Abstract
Aims Dysfunctional matrix turnover is present at sites of abdominal aortic aneurysm (AAA) and leads to the accumulation of monomeric tropoelastin rather than cross-linked elastin. We used a gadolinium-based tropoelastin-specific magnetic resonance contrast agent (Gd-TESMA) to test whether quantifying regional tropoelastin turnover correlates with aortic expansion in a murine model. The binding of Gd-TESMA to excised human AAA was also assessed. Methods and results We utilized the angiotensin II (Ang II)-infused apolipoprotein E gene knockout (ApoE-/-) murine model of aortic dilation and performed in vivo imaging of tropoelastin by administering Gd-TESMA followed by late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) and T1 mapping at 3 T, with subsequent ex vivo validation. In a cross-sectional study (n = 66; control = 11, infused = 55) we found that Gd-TESMA enhanced MRI was elevated and confined to dilated aortic segments (control: LGE=0.13 ± 0.04 mm2, control R1= 1.1 ± 0.05 s-1 vs. dilated LGE=1.0 ± 0.4 mm2, dilated R1 =2.4 ± 0.9 s-1) and was greater in segments with medium (8.0 ± 3.8 mm3) and large (10.4 ± 4.1 mm3) compared to small (3.6 ± 2.1 mm3) vessel volume. Furthermore, a proof-of-principle longitudinal study (n = 19) using Gd-TESMA enhanced MRI demonstrated a greater proportion of tropoelastin: elastin expression in dilating compared to non-dilating aortas, which correlated with the rate of aortic expansion. Treatment with pravastatin and aspirin (n = 10) did not reduce tropoelastin turnover (0.87 ± 0.3 mm2 vs. 1.0 ± 0.44 mm2) or aortic dilation (4.86 ± 2.44 mm3 vs. 4.0 ± 3.6 mm3). Importantly, Gd-TESMA-enhanced MRI identified accumulation of tropoelastin in excised human aneurysmal tissue (n = 4), which was confirmed histologically. Conclusion Tropoelastin MRI identifies dysfunctional matrix remodelling that is specifically expressed in regions of aortic aneurysm or dissection and correlates with the development and rate of aortic expansion. Thus, it may provide an additive imaging marker to the serial assessment of luminal diameter for surveillance of patients at risk of or with established aortopathy.
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Affiliation(s)
- Begoña Lavin
- School of Biomedical Engineering and Imaging Sciences, Department of Biomedical Engineering, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK.,Cardiovascular Division, BHF Centre of Excellence, King's College London, London, UK
| | - Sara Lacerda
- School of Biomedical Engineering and Imaging Sciences, Department of Biomedical Engineering, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK.,Cardiovascular Division, BHF Centre of Excellence, King's College London, London, UK.,Centre de Biophysique Moléculaire, CNRS, Orléans, France
| | - Marcelo E Andia
- School of Biomedical Engineering and Imaging Sciences, Department of Biomedical Engineering, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK.,Radiology Department, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Silvia Lorrio
- School of Biomedical Engineering and Imaging Sciences, Department of Biomedical Engineering, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK.,Cardiovascular Division, BHF Centre of Excellence, King's College London, London, UK
| | - Robert Bakewell
- School of Biomedical Engineering and Imaging Sciences, Department of Biomedical Engineering, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK
| | - Alberto Smith
- Cardiovascular Division, Academic Department of Vascular Surgery, King's College London, London, UK
| | - Imran Rashid
- School of Biomedical Engineering and Imaging Sciences, Department of Biomedical Engineering, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, Department of Biomedical Engineering, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK.,Cardiovascular Division, BHF Centre of Excellence, King's College London, London, UK.,Wellcome Trust and EPSRC Medical Engineering Center, King's College London, London, UK.,Pontificia Universidad Católica de Chile, Escuela de Ingeniería, Santiago, Chile
| | - Alkystis Phinikaridou
- School of Biomedical Engineering and Imaging Sciences, Department of Biomedical Engineering, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK.,Cardiovascular Division, BHF Centre of Excellence, King's College London, London, UK
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Cruz G, Jaubert O, Qi H, Bustin A, Milotta G, Schneider T, Koken P, Doneva M, Botnar RM, Prieto C. 3D free-breathing cardiac magnetic resonance fingerprinting. NMR Biomed 2020; 33:e4370. [PMID: 32696590 DOI: 10.1002/nbm.4370] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [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: 02/12/2020] [Revised: 06/04/2020] [Accepted: 06/23/2020] [Indexed: 05/15/2023]
Abstract
PURPOSE To develop a novel respiratory motion compensated three-dimensional (3D) cardiac magnetic resonance fingerprinting (cMRF) approach for whole-heart myocardial T1 and T2 mapping from a free-breathing scan. METHODS Two-dimensional (2D) cMRF has been recently proposed for simultaneous, co-registered T1 and T2 mapping from a breath-hold scan; however, coverage is limited. Here we propose a novel respiratory motion compensated 3D cMRF approach for whole-heart myocardial T1 and T2 tissue characterization from a free-breathing scan. Variable inversion recovery and T2 preparation modules are used for parametric encoding, respiratory bellows driven localized autofocus is proposed for beat-to-beat translation motion correction and a subspace regularized reconstruction is employed to accelerate the scan. The proposed 3D cMRF approach was evaluated in a standardized T1 /T2 phantom in comparison with reference spin echo values and in 10 healthy subjects in comparison with standard 2D MOLLI, SASHA and T2 -GraSE mapping techniques at 1.5 T. RESULTS 3D cMRF T1 and T2 measurements were generally in good agreement with reference spin echo values in the phantom experiments, with relative errors of 2.9% and 3.8% for T1 and T2 (T2 < 100 ms), respectively. in vivo left ventricle (LV) myocardial T1 values were 1054 ± 19 ms for MOLLI, 1146 ± 20 ms for SASHA and 1093 ± 24 ms for the proposed 3D cMRF; corresponding T2 values were 51.8 ± 1.6 ms for T2-GraSE and 44.6 ± 2.0 ms for 3D cMRF. LV coefficients of variation were 7.6 ± 1.6% for MOLLI, 12.1 ± 2.7% for SASHA and 5.8 ± 0.8% for 3D cMRF T1 , and 10.5 ± 1.4% for T2-GraSE and 11.7 ± 1.6% for 3D cMRF T2 . CONCLUSION The proposed 3D cMRF can provide whole-heart, simultaneous and co-registered T1 and T2 maps with accuracy and precision comparable to those of clinical standards in a single free-breathing scan of about 7 min.
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Affiliation(s)
- Gastão Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Olivier Jaubert
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Haikun Qi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Aurélien Bustin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Giorgia Milotta
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | | | | | | | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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42
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Küstner T, Fuin N, Hammernik K, Bustin A, Qi H, Hajhosseiny R, Masci PG, Neji R, Rueckert D, Botnar RM, Prieto C. CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions. Sci Rep 2020; 10:13710. [PMID: 32792507 PMCID: PMC7426830 DOI: 10.1038/s41598-020-70551-8] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [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: 04/05/2020] [Accepted: 07/31/2020] [Indexed: 11/29/2022] Open
Abstract
Cardiac CINE magnetic resonance imaging is the gold-standard for the assessment of cardiac function. Imaging accelerations have shown to enable 3D CINE with left ventricular (LV) coverage in a single breath-hold. However, 3D imaging remains limited to anisotropic resolution and long reconstruction times. Recently deep learning has shown promising results for computationally efficient reconstructions of highly accelerated 2D CINE imaging. In this work, we propose a novel 4D (3D + time) deep learning-based reconstruction network, termed 4D CINENet, for prospectively undersampled 3D Cartesian CINE imaging. CINENet is based on (3 + 1)D complex-valued spatio-temporal convolutions and multi-coil data processing. We trained and evaluated the proposed CINENet on in-house acquired 3D CINE data of 20 healthy subjects and 15 patients with suspected cardiovascular disease. The proposed CINENet network outperforms iterative reconstructions in visual image quality and contrast (+ 67% improvement). We found good agreement in LV function (bias ± 95% confidence) in terms of end-systolic volume (0 ± 3.3 ml), end-diastolic volume (− 0.4 ± 2.0 ml) and ejection fraction (0.1 ± 3.2%) compared to clinical gold-standard 2D CINE, enabling single breath-hold isotropic 3D CINE in less than 10 s scan and ~ 5 s reconstruction time.
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Affiliation(s)
- Thomas Küstner
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, Lambeth Wing, London, UK.
| | - Niccolo Fuin
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, Lambeth Wing, London, UK
| | | | - Aurelien Bustin
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, Lambeth Wing, London, UK
| | - Haikun Qi
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, Lambeth Wing, London, UK
| | - Reza Hajhosseiny
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, Lambeth Wing, London, UK
| | - Pier Giorgio Masci
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, Lambeth Wing, London, UK
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, Lambeth Wing, London, UK.,MR Research Collaborations, Siemens Healthcare Limited, Frimley, UK
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, UK
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, Lambeth Wing, London, UK.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, Lambeth Wing, London, UK.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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Perry HL, Yoon IC, Chabloz NG, Molisso S, Stasiuk GJ, Botnar RM, Wilton-Ely JDET. Metallostar Assemblies Based on Dithiocarbamates for Use as MRI Contrast Agents. Inorg Chem 2020; 59:10813-10823. [PMID: 32677827 DOI: 10.1021/acs.inorgchem.0c01318] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Two different octadentate gadolinium chelates based on DO3A and DOTAGA chelates (hydration number q = 1) have been used to prepare a series of bi-, tri-, and tetrametallic d-f mixed-metal complexes. The piperazine-based dithiocarbamate linker ensures that rotation of the gadolinium chelates is restricted, leading to enhanced relaxivity (r1) values, which increase with the overall mass and number of gadolinium units. The r1 value (at 10 MHz, 25 °C) per gadolinium unit rises from 5.0 mM-1 s-1 for the Gd-DO3A-NH2 monogadolinium chelate to 9.2 mM-1 s-1 in a trigadolinium complex with a ruthenium(III) core. Using a 1.5 T clinical scanner operating at 63.87 MHz (25 °C), an 86% increase in the relaxivity per gadolinium unit is observed for this multimetallic compound compared to clinically approved Dotarem. The gadolinium complexes based on the DOTAGA chelate also performed well at 63.87 MHz, with a relaxivity value of 9.5 mM-1 s-1 per gadolinium unit being observed for the trigadolinium d-f mixed-metal complex with a ruthenium(III) core. The versatility of dithiocarbamate coordination chemistry thus provides access to a wide range of d-f hybrids with potential for use as high-performance MRI contrast agents.
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Affiliation(s)
- Hannah L Perry
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, White City Campus, London W12 0BZ, U.K.,School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London SE1 7EH, U.K
| | - Il-Chul Yoon
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, White City Campus, London W12 0BZ, U.K
| | - Nicolas G Chabloz
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, White City Campus, London W12 0BZ, U.K
| | - Susannah Molisso
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, White City Campus, London W12 0BZ, U.K
| | - Graeme J Stasiuk
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London SE1 7EH, U.K
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London SE1 7EH, U.K
| | - James D E T Wilton-Ely
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, White City Campus, London W12 0BZ, U.K
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Abstract
Myocardial infarction and stroke are the most prevalent global causes of death. Each year 15 million people worldwide die due to myocardial infarction or stroke. Rupture of a vulnerable atherosclerotic plaque is the main underlying cause of stroke and myocardial infarction. Key features of a vulnerable plaque are inflammation, a large lipid-rich necrotic core (LRNC) with a thin or ruptured overlying fibrous cap, and intraplaque hemorrhage (IPH). Noninvasive imaging of these features could have a role in risk stratification of myocardial infarction and stroke and can potentially be utilized for treatment guidance and monitoring. The recent development of hybrid PET/MRI combining the superior soft tissue contrast of MRI with the opportunity to visualize specific plaque features using various radioactive tracers, paves the way for comprehensive plaque imaging. In this review, the use of hybrid PET/MRI for atherosclerotic plaque imaging in carotid and coronary arteries is discussed. The pros and cons of different hybrid PET/MRI systems are reviewed. The challenges in the development of PET/MRI and potential solutions are described. An overview of PET and MRI acquisition techniques for imaging of atherosclerosis including motion correction is provided, followed by a summary of vessel wall imaging PET/MRI studies in patients with carotid and coronary artery disease. Finally, the future of imaging of atherosclerosis with PET/MRI is discussed.
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Affiliation(s)
- Mueez Aizaz
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.,CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Rik P M Moonen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.,CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Jochem A J van der Pol
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Escuela de Ingenieria, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Escuela de Ingenieria, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - M Eline Kooi
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.,CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
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45
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Munoz C, Bustin A, Neji R, Kunze KP, Forman C, Schmidt M, Hajhosseiny R, Masci PG, Zeilinger M, Wuest W, Botnar RM, Prieto C. Motion-corrected 3D whole-heart water-fat high-resolution late gadolinium enhancement cardiovascular magnetic resonance imaging. J Cardiovasc Magn Reson 2020; 22:53. [PMID: 32684167 PMCID: PMC7370486 DOI: 10.1186/s12968-020-00649-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [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: 10/03/2019] [Accepted: 06/17/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Conventional 2D inversion recovery (IR) and phase sensitive inversion recovery (PSIR) late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) have been widely incorporated into routine CMR for the assessment of myocardial viability. However, reliable suppression of fat signal, and increased isotropic spatial resolution and volumetric coverage within a clinically feasible scan time remain a challenge. In order to address these challenges, this work proposes a highly efficient respiratory motion-corrected 3D whole-heart water/fat LGE imaging framework. METHODS An accelerated IR-prepared 3D dual-echo acquisition and motion-corrected reconstruction framework for whole-heart water/fat LGE imaging was developed. The acquisition sequence includes 2D image navigators (iNAV), which are used to track the respiratory motion of the heart and enable 100% scan efficiency. Non-rigid motion information estimated from the 2D iNAVs and from the data itself is integrated into a high-dimensional patch-based undersampled reconstruction technique (HD-PROST), to produce high-resolution water/fat 3D LGE images. A cohort of 20 patients with known or suspected cardiovascular disease was scanned with the proposed 3D water/fat LGE approach. 3D water LGE images were compared to conventional breath-held 2D LGE images (2-chamber, 4-chamber and stack of short-axis views) in terms of image quality (1: full diagnostic to 4: non-diagnostic) and presence of LGE findings. RESULTS Image quality was considered diagnostic in 18/20 datasets for both 2D and 3D LGE magnitude images, with comparable image quality scores (2D: 2.05 ± 0.72, 3D: 1.88 ± 0.90, p-value = 0.62) and overall agreement in LGE findings. Acquisition time for isotropic high-resolution (1.3mm3) water/fat LGE images was 8.0 ± 1.4 min (3-fold acceleration, 60-88 slices covering the whole heart), while 2D LGE images were acquired in 5.6 ± 2.2 min (12-18 slices, including pauses between breath-holds) albeit with a lower spatial resolution (1.40-1.75 mm in-plane × 8 mm slice thickness). CONCLUSION A novel framework for motion-corrected whole-heart 3D water/fat LGE imaging has been introduced. The method was validated in patients with known or suspected cardiovascular disease, showing good agreement with conventional breath-held 2D LGE imaging, but offering higher spatial resolution, improved volumetric coverage and good image quality from a free-breathing acquisition with 100% scan efficiency and predictable scan time.
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Affiliation(s)
- Camila Munoz
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK.
| | - Aurelien Bustin
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
- MR Research Collaborations, Siemens Healthcare, Frimley, UK
| | - Karl P Kunze
- MR Research Collaborations, Siemens Healthcare, Frimley, UK
| | - Christoph Forman
- Cardiovascular MR Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Michaela Schmidt
- Cardiovascular MR Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Reza Hajhosseiny
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Pier-Giorgio Masci
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Martin Zeilinger
- Institute of Radiology, University Hospital Erlangen, Erlangen, Germany
| | - Wolfgang Wuest
- Institute of Radiology, University Hospital Erlangen, Erlangen, Germany
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
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46
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Milotta G, Bustin A, Jaubert O, Neji R, Prieto C, Botnar RM. 3D whole-heart isotropic-resolution motion-compensated joint T 1 /T 2 mapping and water/fat imaging. Magn Reson Med 2020; 84:3009-3026. [PMID: 32544278 DOI: 10.1002/mrm.28330] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [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: 01/23/2020] [Revised: 04/27/2020] [Accepted: 04/28/2020] [Indexed: 01/12/2023]
Abstract
PURPOSE To develop a free-breathing isotropic-resolution whole-heart joint T1 and T2 mapping sequence with Dixon-encoding that provides coregistered 3D T1 and T2 maps and complementary 3D anatomical water and fat images in a single ~9 min scan. METHODS Four interleaved dual-echo Dixon gradient echo volumes are acquired with a variable density Cartesian trajectory and different preparation pulses: 1) inversion recovery-preparation, 2) and 3) no preparations, and 4) T2 preparation. Image navigators are acquired to correct each echo for 2D translational respiratory motion; the 8 echoes are jointly reconstructed with a low-rank patch-based reconstruction. A water/fat separation algorithm is used to obtain water and fat images for each acquired volume. T1 and T2 maps are generated by matching the signal evolution of the water images to a simulated dictionary. Complementary bright-blood and fat volumes for anatomical visualization are obtained from the T2 -prepared dataset. The proposed sequence was tested in phantom experiments and 10 healthy subjects and compared to standard 2D MOLLI T1 mapping, 2D balance steady-state free precession T2 mapping, and 3D T2 -prepared Dixon coronary MR angiography. RESULTS High linear correlation was found between T1 and T2 quantification with the proposed approach and phantom spin echo measurements (y = 1.1 × -11.68, R2 = 0.98; and y = 0.85 × +5.7, R2 = 0.99). Mean myocardial values of T1 /T2 = 1116 ± 30.5 ms/45.1 ± 2.38 ms were measured in vivo. Biases of T1 /T2 = 101.8 ms/-0.77 ms were obtained compared to standard 2D techniques. CONCLUSION The proposed joint T1 /T2 sequence permitted the acquisition of motion-compensated isotropic-resolution 3D T1 and T2 maps and complementary coronary MR angiography and fat volumes, showing promising results in terms of T1 and T2 quantification and visualization of cardiac anatomy and pericardial fat.
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Affiliation(s)
- Giorgia Milotta
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Aurelien Bustin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Olivier Jaubert
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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47
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Bustin A, Rashid I, Cruz G, Hajhosseiny R, Correia T, Neji R, Rajani R, Ismail TF, Botnar RM, Prieto C. 3D whole-heart isotropic sub-millimeter resolution coronary magnetic resonance angiography with non-rigid motion-compensated PROST. J Cardiovasc Magn Reson 2020; 22:24. [PMID: 32299445 PMCID: PMC7161114 DOI: 10.1186/s12968-020-00611-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [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: 07/19/2019] [Accepted: 02/19/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND To enable free-breathing whole-heart sub-millimeter resolution coronary magnetic resonance angiography (CMRA) in a clinically feasible scan time by combining low-rank patch-based undersampled reconstruction (3D-PROST) with a highly accelerated non-rigid motion correction framework. METHODS Non-rigid motion corrected CMRA combined with 2D image-based navigators has been previously proposed to enable 100% respiratory scan efficiency in modestly undersampled acquisitions. Achieving sub-millimeter isotropic resolution with such techniques still requires prohibitively long acquisition times. We propose to combine 3D-PROST reconstruction with a highly accelerated non-rigid motion correction framework to achieve sub-millimeter resolution CMRA in less than 10 min. Ten healthy subjects and eight patients with suspected coronary artery disease underwent 4-5-fold accelerated free-breathing whole-heart CMRA with 0.9 mm3 isotropic resolution. Vessel sharpness, vessel length and image quality obtained with the proposed non-rigid (NR) PROST approach were compared against translational correction only (TC-PROST) and a previously proposed NR motion-compensated technique (non-rigid SENSE) in healthy subjects. For the patient study, image quality scoring and visual comparison with coronary computed tomography angiography (CCTA) were performed. RESULTS Average scan times [min:s] were 6:01 ± 0:59 (healthy subjects) and 8:29 ± 1:41 (patients). In healthy subjects, vessel sharpness of the left anterior descending (LAD) and right (RCA) coronary arteries were improved with the proposed non-rigid PROST (LAD: 51.2 ± 8.8%, RCA: 61.2 ± 9.1%) in comparison to TC-PROST (LAD: 43.8 ± 5.1%, P = 0.051, RCA: 54.3 ± 8.3%, P = 0.218) and non-rigid SENSE (LAD: 46.1 ± 5.8%, P = 0.223, RCA: 56.7 ± 9.6%, P = 0.50), although differences were not statistically significant. The average visual image quality score was significantly higher for NR-PROST (LAD: 3.2 ± 0.6, RCA: 3.3 ± 0.7) compared with TC-PROST (LAD: 2.1 ± 0.6, P = 0.018, RCA: 2.0 ± 0.7, P = 0.014) and non-rigid SENSE (LAD: 2.3 ± 0.5, P = 0.008, RCA: 2.5 ± 0.7, P = 0.016). In patients, the proposed approach showed good delineation of the coronaries, in agreement with CCTA, with image quality scores and vessel sharpness similar to that of healthy subjects. CONCLUSIONS We demonstrate the feasibility of combining high undersampling factors with non-rigid motion-compensated reconstruction to obtain high-quality sub-millimeter isotropic CMRA images in ~ 8 min. Validation in a larger cohort of patients with coronary artery disease is now warranted.
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Affiliation(s)
- Aurélien Bustin
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Imran Rashid
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Gastao Cruz
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Reza Hajhosseiny
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Teresa Correia
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Radhouene Neji
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, UK
| | - Ronak Rajani
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
- Department of Cardiology, Guy's & St Thomas' Hospitals, London, UK
| | - Tevfik F Ismail
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
| | - René M Botnar
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK.
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile.
| | - Claudia Prieto
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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Nordio G, Bustin A, Odille F, Schneider T, Henningsson M, Prieto C, Botnar RM. Faster 3D saturation-recovery based myocardial T1 mapping using a reduced number of saturation points and denoising. PLoS One 2020; 15:e0221071. [PMID: 32275668 PMCID: PMC7147792 DOI: 10.1371/journal.pone.0221071] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Accepted: 03/21/2020] [Indexed: 11/19/2022] Open
Abstract
Purpose To accelerate the acquisition of free-breathing 3D saturation-recovery-based (SASHA) myocardial T1 mapping by acquiring fewer saturation points in combination with a post-processing 3D denoising technique to maintain high accuracy and precision. Methods 3D SASHA T1 mapping acquires nine T1-weighted images along the saturation recovery curve, resulting in long acquisition times. In this work, we propose to accelerate conventional cardiac T1 mapping by reducing the number of saturation points. High T1 accuracy and low standard deviation (as a surrogate for precision) is maintained by applying a 3D denoising technique to the T1-weighted images prior to pixel-wise T1 fitting. The proposed approach was evaluated on a T1 phantom and 20 healthy subjects, by varying the number of T1-weighted images acquired between three and nine, both prospectively and retrospectively. Following the results from the healthy subjects, three patients with suspected cardiovascular disease were acquired using five T1-weighted images. T1 accuracy and precision was determined for all the acquisitions before and after denoising. Results In the T1 phantom, no statistical difference was found in terms of accuracy and precision for the different number of T1-weighted images before or after denoising (P = 0.99 and P = 0.99 for accuracy, P = 0.64 and P = 0.42 for precision, respectively). In vivo, both prospectively and retrospectively, the precision improved considerably with the number of T1-weighted images employed before denoising (P<0.05) but was independent on the number of T1-weighted images after denoising. Conclusion We demonstrate the feasibility of accelerating 3D SASHA T1 mapping by reducing the number of acquired T1-weighted images in combination with an efficient 3D denoising, without affecting accuracy and precision of T1 values.
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Affiliation(s)
- Giovanna Nordio
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, England, United Kingdom
- * E-mail:
| | - Aurelien Bustin
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, England, United Kingdom
| | - Freddy Odille
- CIC-IT 1433, INSERM, Université de Lorraine and CHRU de Nancy, Nancy, France
- IADI, INSERM U1254 and Université de Lorraine, Nancy, France
| | | | - Markus Henningsson
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, England, United Kingdom
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, England, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - René M. Botnar
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, England, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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Chabloz NG, Perry HL, Yoon IC, Coulson AJ, White AJP, Stasiuk GJ, Botnar RM, Wilton-Ely JDET. Combined Magnetic Resonance Imaging and Photodynamic Therapy Using Polyfunctionalised Nanoparticles Bearing Robust Gadolinium Surface Units. Chemistry 2020; 26:4552-4566. [PMID: 31981387 DOI: 10.1002/chem.201904757] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [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: 10/18/2019] [Indexed: 12/12/2022]
Abstract
A robust dithiocarbamate tether allows novel gadolinium units based on DOTAGA (q=1) to be attached to the surface of gold nanoparticles (2.6-4.1 nm diameter) along with functional units offering biocompatibility, targeting and photodynamic therapy. A dramatic increase in relaxivity (r1 ) per Gd unit from 5.01 mm-1 s-1 in unbound form to 31.68 mm-1 s-1 (10 MHz, 37 °C) is observed when immobilised on the surface due to restricted rotation and enhanced rigidity of the Gd complex on the nanoparticle surface. The single-step synthetic route provides a straightforward and versatile way of preparing multifunctional gold nanoparticles, including examples with conjugated zinc-tetraphenylporphyrin photosensitizers. The lack of toxicity of these materials (MTT assays) is transformed on irradiation of HeLa cells for 30 minutes (PDT), leading to 75 % cell death. In addition to passive targeting, the inclusion of units capable of actively targeting overexpressed folate receptors illustrates the potential of these assemblies as targeted theranostic agents.
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Affiliation(s)
- Nicolas G Chabloz
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, White City Campus, London, W12 0BZ, UK
| | - Hannah L Perry
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, White City Campus, London, W12 0BZ, UK.,Division of Imaging Sciences and Biomedical Engineering, King's College London, St Thomas' Hospital, London, SE1 7EH, UK
| | - Il-Chul Yoon
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, White City Campus, London, W12 0BZ, UK
| | - Andrew J Coulson
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, White City Campus, London, W12 0BZ, UK
| | - Andrew J P White
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, White City Campus, London, W12 0BZ, UK
| | - Graeme J Stasiuk
- School of Life Sciences, Biomedical Sciences, University of Hull, Hull, HU6 7RX, UK
| | - René M Botnar
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St Thomas' Hospital, London, SE1 7EH, UK
| | - James D E T Wilton-Ely
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, White City Campus, London, W12 0BZ, UK.,London Centre for Nanotechnology (LCN), London, UK
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50
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Bustin A, Fuin N, Botnar RM, Prieto C. From Compressed-Sensing to Artificial Intelligence-Based Cardiac MRI Reconstruction. Front Cardiovasc Med 2020; 7:17. [PMID: 32158767 PMCID: PMC7051921 DOI: 10.3389/fcvm.2020.00017] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [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: 09/30/2019] [Accepted: 01/31/2020] [Indexed: 12/28/2022] Open
Abstract
Cardiac magnetic resonance (CMR) imaging is an important tool for the non-invasive assessment of cardiovascular disease. However, CMR suffers from long acquisition times due to the need of obtaining images with high temporal and spatial resolution, different contrasts, and/or whole-heart coverage. In addition, both cardiac and respiratory-induced motion of the heart during the acquisition need to be accounted for, further increasing the scan time. Several undersampling reconstruction techniques have been proposed during the last decades to speed up CMR acquisition. These techniques rely on acquiring less data than needed and estimating the non-acquired data exploiting some sort of prior information. Parallel imaging and compressed sensing undersampling reconstruction techniques have revolutionized the field, enabling 2- to 3-fold scan time accelerations to become standard in clinical practice. Recent scientific advances in CMR reconstruction hinge on the thriving field of artificial intelligence. Machine learning reconstruction approaches have been recently proposed to learn the non-linear optimization process employed in CMR reconstruction. Unlike analytical methods for which the reconstruction problem is explicitly defined into the optimization process, machine learning techniques make use of large data sets to learn the key reconstruction parameters and priors. In particular, deep learning techniques promise to use deep neural networks (DNN) to learn the reconstruction process from existing datasets in advance, providing a fast and efficient reconstruction that can be applied to all newly acquired data. However, before machine learning and DNN can realize their full potentials and enter widespread clinical routine for CMR image reconstruction, there are several technical hurdles that need to be addressed. In this article, we provide an overview of the recent developments in the area of artificial intelligence for CMR image reconstruction. The underlying assumptions of established techniques such as compressed sensing and low-rank reconstruction are briefly summarized, while a greater focus is given to recent advances in dictionary learning and deep learning based CMR reconstruction. In particular, approaches that exploit neural networks as implicit or explicit priors are discussed for 2D dynamic cardiac imaging and 3D whole-heart CMR imaging. Current limitations, challenges, and potential future directions of these techniques are also discussed.
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Affiliation(s)
- Aurélien Bustin
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Niccolo Fuin
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - René M. Botnar
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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