  Motion and deformation analysis of cardiac magnetic resonance (CMR) imaging
videos is crucial for assessing myocardial strain of patients with abnormal
heart functions. Recent advances in deep learning-based image registration
algorithms have shown promising results in predicting motion fields from
routinely acquired CMR sequences. However, their accuracy often diminishes in
regions with subtle appearance change, with errors propagating over time.
Advanced imaging techniques, such as displacement encoding with stimulated
echoes (DENSE) CMR, offer highly accurate and reproducible motion data but
require additional image acquisition, which poses challenges in busy clinical
flows. In this paper, we introduce a novel Latent Motion Diffusion model
(LaMoD) to predict highly accurate DENSE motions from standard CMR videos. More
specifically, our method first employs an encoder from a pre-trained
registration network that learns latent motion features (also considered as
deformation-based shape features) from image sequences. Supervised by the
ground-truth motion provided by DENSE, LaMoD then leverages a probabilistic
latent diffusion model to reconstruct accurate motion from these extracted
features. Experimental results demonstrate that our proposed method, LaMoD,
significantly improves the accuracy of motion analysis in standard CMR images;
hence improving myocardial strain analysis in clinical settings for cardiac
patients. Our code will be publicly available on upon acceptance.
