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Roehl M, Conway M, Ghonim S, Ferreira PF, Nielles-Vallespin S, Babu-Narayan SV, Pennell DJ, Gatehouse PD, Scott AD. STEAM-SASHA: a novel approach for blood- and fat-suppressed native T1 measurement in the right ventricular myocardium. MAGMA (NEW YORK, N.Y.) 2024; 37:295-305. [PMID: 38216813 PMCID: PMC10995026 DOI: 10.1007/s10334-023-01141-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 12/07/2023] [Accepted: 12/11/2023] [Indexed: 01/14/2024]
Abstract
OBJECTIVE The excellent blood and fat suppression of stimulated echo acquisition mode (STEAM) can be combined with saturation recovery single-shot acquisition (SASHA) in a novel STEAM-SASHA sequence for right ventricular (RV) native T1 mapping. MATERIALS AND METHODS STEAM-SASHA splits magnetization preparation over two cardiac cycles, nulling blood signal and allowing fat signal to decay. Breath-hold T1 mapping was performed in a T1 phantom and twice in 10 volunteers using STEAM-SASHA and a modified Look-Locker sequence at peak systole at 3T. T1 was measured in 3 RV regions, the septum and left ventricle (LV). RESULTS In phantoms, MOLLI under-estimated while STEAM-SASHA over-estimated T1, on average by 3.0% and 7.0% respectively, although at typical 3T myocardial T1 (T1 > 1200 ms) STEAM-SASHA was more accurate. In volunteers, T1 was higher using STEAM-SASHA than MOLLI in the LV and septum (p = 0.03, p = 0.006, respectively), but lower in RV regions (p > 0.05). Inter-study, inter-observer and intra-observer coefficients of variation in all regions were < 15%. Blood suppression was excellent with STEAM-SASHA and noise floor effects were minimal. DISCUSSION STEAM-SASHA provides accurate and reproducible T1 in the RV with excellent blood and fat suppression. STEAM-SASHA has potential to provide new insights into pathological changes in the RV in future studies.
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Affiliation(s)
- Malte Roehl
- Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, Guy's and St Thomas' NHS Foundation Trust, Sydney Street, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Miriam Conway
- Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, Guy's and St Thomas' NHS Foundation Trust, Sydney Street, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Sarah Ghonim
- Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, Guy's and St Thomas' NHS Foundation Trust, Sydney Street, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Pedro F Ferreira
- Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, Guy's and St Thomas' NHS Foundation Trust, Sydney Street, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Sonia Nielles-Vallespin
- Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, Guy's and St Thomas' NHS Foundation Trust, Sydney Street, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Sonya V Babu-Narayan
- Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, Guy's and St Thomas' NHS Foundation Trust, Sydney Street, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Dudley J Pennell
- Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, Guy's and St Thomas' NHS Foundation Trust, Sydney Street, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Peter D Gatehouse
- Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, Guy's and St Thomas' NHS Foundation Trust, Sydney Street, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Andrew D Scott
- Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, Guy's and St Thomas' NHS Foundation Trust, Sydney Street, London, UK.
- National Heart and Lung Institute, Imperial College London, London, UK.
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Huang J, Ferreira PF, Wang L, Wu Y, Aviles-Rivero AI, Schönlieb CB, Scott AD, Khalique Z, Dwornik M, Rajakulasingam R, De Silva R, Pennell DJ, Nielles-Vallespin S, Yang G. Deep learning-based diffusion tensor cardiac magnetic resonance reconstruction: a comparison study. Sci Rep 2024; 14:5658. [PMID: 38454072 PMCID: PMC10920645 DOI: 10.1038/s41598-024-55880-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 02/27/2024] [Indexed: 03/09/2024] Open
Abstract
In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI) technique for evaluating the microstructure of myocardial tissue in living hearts, providing insights into cardiac function and enabling the development of innovative therapeutic strategies. However, the integration of cDTI into routine clinical practice poses challenging due to the technical obstacles involved in the acquisition, such as low signal-to-noise ratio and prolonged scanning times. In this study, we investigated and implemented three different types of deep learning-based MRI reconstruction models for cDTI reconstruction. We evaluated the performance of these models based on the reconstruction quality assessment, the diffusion tensor parameter assessment as well as the computational cost assessment. Our results indicate that the models discussed in this study can be applied for clinical use at an acceleration factor (AF) of × 2 and × 4 , with the D5C5 model showing superior fidelity for reconstruction and the SwinMR model providing higher perceptual scores. There is no statistical difference from the reference for all diffusion tensor parameters at AF × 2 or most DT parameters at AF × 4 , and the quality of most diffusion tensor parameter maps is visually acceptable. SwinMR is recommended as the optimal approach for reconstruction at AF × 2 and AF × 4 . However, we believe that the models discussed in this study are not yet ready for clinical use at a higher AF. At AF × 8 , the performance of all models discussed remains limited, with only half of the diffusion tensor parameters being recovered to a level with no statistical difference from the reference. Some diffusion tensor parameter maps even provide wrong and misleading information.
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Grants
- Wellcome Trust
- RG/19/1/34160 British Heart Foundation
- This study was supported in part by the UKRI Future Leaders Fellowship (MR/V023799/1), BHF (RG/19/1/34160), the ERC IMI (101005122), the H2020 (952172), the MRC (MC/PC/21013), the Royal Society (IEC/NSFC/211235), the NVIDIA Academic Hardware Grant Program, EPSRC (EP/V029428/1, EP/S026045/1, EP/T003553/1, EP/N014588/1, EP/T017961/1), and the Cambridge Mathematics of Information in Healthcare Hub (CMIH) Partnership Fund.
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Affiliation(s)
- Jiahao Huang
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK.
- Bioengineering Department and Imperial-X, Imperial College London, London, W12 7SL, UK.
| | - Pedro F Ferreira
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Lichao Wang
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Department of Computing, Imperial College London, London, UK
| | - Yinzhe Wu
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Angelica I Aviles-Rivero
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Andrew D Scott
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Zohya Khalique
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Maria Dwornik
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Ramyah Rajakulasingam
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Ranil De Silva
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Dudley J Pennell
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Sonia Nielles-Vallespin
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK.
- Bioengineering Department and Imperial-X, Imperial College London, London, W12 7SL, UK.
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Wang M, Ching-Johnson JA, Yin H, O’Neil C, Li AX, Chu MWA, Bartha R, Pickering JG. Mapping microarchitectural degeneration in the dilated ascending aorta with ex vivo diffusion tensor imaging. EUROPEAN HEART JOURNAL OPEN 2024; 4:oead128. [PMID: 38162403 PMCID: PMC10755346 DOI: 10.1093/ehjopen/oead128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 10/26/2023] [Accepted: 11/30/2023] [Indexed: 01/03/2024]
Abstract
Aims Thoracic aortic aneurysms (TAAs) carry a risk of catastrophic dissection. Current strategies to evaluate this risk entail measuring aortic diameter but do not image medial degeneration, the cause of TAAs. We sought to determine if the advanced magnetic resonance imaging (MRI) acquisition strategy, diffusion tensor imaging (DTI), could delineate medial degeneration in the ascending thoracic aorta. Methods and results Porcine ascending aortas were subjected to enzyme microinjection, which yielded local aortic medial degeneration. These lesions were detected by DTI, using a 9.4 T MRI scanner, based on tensor disorientation, disrupted diffusion tracts, and altered DTI metrics. High-resolution spatial analysis revealed that fractional anisotropy positively correlated, and mean and radial diffusivity inversely correlated, with smooth muscle cell (SMC) and elastin content (P < 0.001 for all). Ten operatively harvested human ascending aorta samples (mean subject age 61.6 ± 13.3 years, diameter range 29-64 mm) showed medial pathology that was more diffuse and more complex. Nonetheless, DTI metrics within an aorta spatially correlated with SMC, elastin, and, especially, glycosaminoglycan (GAG) content. Moreover, there were inter-individual differences in slice-averaged DTI metrics. Glycosaminoglycan accumulation and elastin degradation were captured by reduced fractional anisotropy (R2 = 0.47, P = 0.043; R2 = 0.76, P = 0.002), with GAG accumulation also captured by increased mean diffusivity (R2 = 0.46, P = 0.045) and increased radial diffusivity (R2 = 0.60, P = 0.015). Conclusion Ex vivo high-field DTI can detect ascending aorta medial degeneration and can differentiate TAAs in accordance with their histopathology, especially elastin and GAG changes. This non-destructive window into aortic medial microstructure raises prospects for probing the risks of TAAs beyond lumen dimensions.
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Affiliation(s)
- Mofei Wang
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, 1151 Richmond St. N. London, Canada, N6A 5B7
- Department of Biochemistry, Western University, 1151 Richmond St. N. London, Canada, N6A 3K7
| | - Justin A Ching-Johnson
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, 1151 Richmond St. N. London, Canada, N6A 5B7
- Department of Medical Biophysics, Western University, 1151 Richmond St. N. London, Canada, N6A 3K7
| | - Hao Yin
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, 1151 Richmond St. N. London, Canada, N6A 5B7
| | - Caroline O’Neil
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, 1151 Richmond St. N. London, Canada, N6A 5B7
| | - Alex X Li
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, 1151 Richmond St. N. London, Canada, N6A 5B7
| | - Michael W A Chu
- Department of Surgery, Western University, 1151 Richmond St. N. London, Canada, N6A 3K7
- London Health Sciences Centre, 339 Windermere Rd, London, Ontario, Canada, N6A 5A5
| | - Robert Bartha
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, 1151 Richmond St. N. London, Canada, N6A 5B7
- Department of Medical Biophysics, Western University, 1151 Richmond St. N. London, Canada, N6A 3K7
| | - J Geoffrey Pickering
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, 1151 Richmond St. N. London, Canada, N6A 5B7
- Department of Biochemistry, Western University, 1151 Richmond St. N. London, Canada, N6A 3K7
- Department of Medical Biophysics, Western University, 1151 Richmond St. N. London, Canada, N6A 3K7
- London Health Sciences Centre, 339 Windermere Rd, London, Ontario, Canada, N6A 5A5
- Department of Medicine, Western University, 1151 Richmond St. N. London, Canada N6A 3K7
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Xu X, Hu J, Zheng Y, Liu Y, Cui Z, Liang D, Zhu Y. Slice-specific tracking for free-breathing diffusion tensor cardiac MRI. NMR IN BIOMEDICINE 2023:e4922. [PMID: 36914257 DOI: 10.1002/nbm.4922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 02/20/2023] [Accepted: 02/22/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Diffusion tensor cardiac magnetic resonance (DT-CMR) imaging has great potential to characterize myocardial microarchitecture. However, its accuracy is limited by respiratory and cardiac motion and long scan times. Here, we develop and evaluate a slice-specific tracking method to improve the efficiency and accuracy of DT-CMR acquisition during free breathing. METHODS Coronal images were obtained along with signals from a diaphragmatic navigator. Respiratory and slice displacements were obtained from the navigator signals and coronal images, respectively, and these displacements were fitted with a linear model to obtain the slice-specific tracking factors. This method was evaluated in DT-CMR examinations of 17 healthy subjects, and the results were compared with those obtained using a fixed tracking factor of 0.6. DT-CMR with breath-holding was used for reference. Quantitative and qualitative evaluation methods were used to analyze the performance of the slice-specific tracking method and the consistency between the obtained diffusion parameters. RESULTS In the study, the slice-specific tracking factors showed an upward trend from the basal to the apical slice. Residual in-plane movements were lower in slice-specific tracking than in fixed-factor tracking (RMSE: 2.748 ± 1.171 versus 5.983 ± 2.623, P < 0.001). The diffusion parameters obtained using slice-specific tracking were not significantly different from those obtained from breath-holding acquisition (P > 0.05). CONCLUSION In free-breathing DT-CMR imaging, the slice-specific tracking method reduced misalignment of the acquired slices. The diffusion parameters obtained using this approach were consistent with those obtained with the breath-holding technique.
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Affiliation(s)
- Xi Xu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
| | - Junpu Hu
- United Imaging Healthcare, Shanghai, China
| | - Yijia Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yuanyuan Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhuoxu Cui
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Dong Liang
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
- Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yanjie Zhu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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