Groupwise Deformable Registration of Diffusion Tensor Cardiovascular Magnetic Resonance: Disentangling Diffusion Contrast, Respiratory and Cardiac Motions
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| author | Wang, Fanwen Luo, Yihao Wen, Ke Huang, Jiahao Ferreira, Pedro F. Luo, Yaqing Wu, Yinzhe Munoz, Camila Pennell, Dudley J. Scott, Andrew D. Nielles-Vallespin, Sonia Yang, Guang |
| author_facet | Wang, Fanwen Luo, Yihao Wen, Ke Huang, Jiahao Ferreira, Pedro F. Luo, Yaqing Wu, Yinzhe Munoz, Camila Pennell, Dudley J. Scott, Andrew D. Nielles-Vallespin, Sonia Yang, Guang |
| contents | Diffusion tensor based cardiovascular magnetic resonance (DT-CMR) offers a non-invasive method to visualize the myocardial microstructure. With the assumption that the heart is stationary, frames are acquired with multiple repetitions for different diffusion encoding directions. However, motion from poor breath-holding and imprecise cardiac triggering complicates DT-CMR analysis, further challenged by its inherently low SNR, varied contrasts, and diffusion induced textures. Our solution is a novel framework employing groupwise registration with an implicit template to isolate respiratory and cardiac motions, while a tensor-embedded branch preserves diffusion contrast textures. We have devised a loss refinement tailored for non-linear least squares fitting and low SNR conditions. Additionally, we introduce new physics-based and clinical metrics for performance evaluation. Access code and supplementary materials at: https://github.com/ayanglab/DTCMR-Reg |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13788 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Groupwise Deformable Registration of Diffusion Tensor Cardiovascular Magnetic Resonance: Disentangling Diffusion Contrast, Respiratory and Cardiac Motions Wang, Fanwen Luo, Yihao Wen, Ke Huang, Jiahao Ferreira, Pedro F. Luo, Yaqing Wu, Yinzhe Munoz, Camila Pennell, Dudley J. Scott, Andrew D. Nielles-Vallespin, Sonia Yang, Guang Signal Processing Diffusion tensor based cardiovascular magnetic resonance (DT-CMR) offers a non-invasive method to visualize the myocardial microstructure. With the assumption that the heart is stationary, frames are acquired with multiple repetitions for different diffusion encoding directions. However, motion from poor breath-holding and imprecise cardiac triggering complicates DT-CMR analysis, further challenged by its inherently low SNR, varied contrasts, and diffusion induced textures. Our solution is a novel framework employing groupwise registration with an implicit template to isolate respiratory and cardiac motions, while a tensor-embedded branch preserves diffusion contrast textures. We have devised a loss refinement tailored for non-linear least squares fitting and low SNR conditions. Additionally, we introduce new physics-based and clinical metrics for performance evaluation. Access code and supplementary materials at: https://github.com/ayanglab/DTCMR-Reg |
| title | Groupwise Deformable Registration of Diffusion Tensor Cardiovascular Magnetic Resonance: Disentangling Diffusion Contrast, Respiratory and Cardiac Motions |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2406.13788 |