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

TL;DR

This paper introduces a deep learning-based deformable registration method for diffusion tensor cardiac MRI that preserves texture information, improves efficiency, and enhances image alignment despite low signal-to-noise ratios.

Contribution

The study presents a novel B-spline based registration network utilizing low-rank features and variational autoencoder to maintain texture in DT-CMR, addressing challenges of noise and contrast.

Findings

Improved image registration accuracy in DT-CMR

Enhanced texture preservation during registration

Increased computational efficiency and frame utilization

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.