GenStrain: Multi-Conditional Generative Diffusion Model To Synthesize High-quality Motion and Strain Maps From Cardiac Magnetic Resonance Images
This paper presents GenStrain, a novel method for synthesizing high-quality cardiac motion-based strain maps from routinely collected cine cardiac magnetic resonance (CMR) images. Existing approaches typically rely on image registration to estimate motion (a sequence of displacements) from cine CMR images, but cine CMR presents very sparse trackable intramyocardial features, presenting challenges to estimate intramyocardial motion. An advanced imaging technique, Displacement Encoding with Stimulated Echoes (DENSE) CMR, is considered as the gold standard for strain assessment, but it requires additional acquisitions, which are not ideal for efficient clinical workflows. To address this gap, we introduce GenStrain, a novel framework designed to learn the data distribution of highly accurate cardiac motion captured by DENSE. In particular, we develop a diffusion process in motion space, where spatiotemporal continuity is carefully considered. To fully leverage both cine CMR images and their associated motion patterns, we introduce a multi-conditional conditioning strategy that integrates anatomical features and motion representations learned by a spatiotemporal registration model to guide the generation process. Experimental results demonstrate that our proposed model, GenStrain, outperforms existing strain analysis methods based on displacement estimates from both traditional registration approaches and state-of-the-art (SOTA) deep learning models. Our model represents a promising step toward more accurate cardiac function assessment and holds great potential for clinical translation. Our code is publicly available at https://github.com/Rishov-MIA/GenStrain.