Efficient Post-processing of Diffusion Tensor Cardiac Magnetic Imaging Using Texture-conserving Deformable Registration

Fanwen Wang; Pedro F. Ferreira; Yinzhe Wu; Camila Munoz; Ke Wen; Yaqing Luo; Jiahao Huang; Dudley J. Pennell; Andrew D. Scott; Sonia Nielles-Vallespin; Guang Yang

arXiv · 2309.06598

Efficient Post-processing of Diffusion Tensor Cardiac Magnetic Imaging Using Texture-conserving Deformable Registration

Fanwen Wang·Pedro F. Ferreira·Yinzhe Wu·Camila Munoz·Ke Wen·Yaqing Luo·Jiahao Huang·Dudley J. Pennell·Andrew D. Scott·Sonia Nielles-Vallespin·Guang Yang

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Abstract

Diffusion tensor cardiac magnetic resonance (DT-CMR) is a method capable of providing non-invasive measurements of myocardial microstructure. Image registration is essential to correct image shifts due to intra and inter breath-hold motion and imperfect cardiac triggering. Registration is challenging in DT-CMR due to the low signal-to-noise and various contrasts induced by the diffusion encoding in the myocardium and surrounding organs. Traditional deformable registration corrects through-plane motion but at the risk of destroying the texture information while rigid registration inefficiently discards frames with local deformation. In this study, we explored the possibility of deep learning-based deformable registration on DT-CMR. Based on the noise suppression using low-rank features and diffusion encoding suppression using variational auto encoder-decoder, a B-spline based registration network extracted the displacement fields and maintained the texture features of DT-CMR. In this way, our method improved the efficiency of frame utilization, manual cropping, and computational speed.

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Fanwen Wang, Pedro F. Ferreira, Yinzhe Wu, Camila Munoz, Ke Wen, Yaqing Luo, Jiahao Huang, Dudley J. Pennell, Andrew D. Scott, Sonia Nielles-Vallespin, Guang Yang. 2024-05-16. Efficient Post-processing of Diffusion Tensor Cardiac Magnetic Imaging Using Texture-conserving Deformable Registration. https://arxiv.org/abs/2309.06598

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