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.

翻译：心脏磁共振（CMR）成像视频的运动与形变分析对于评估心功能异常患者的心肌应变至关重要。基于深度学习的图像配准算法的最新进展，在从常规采集的CMR序列中预测运动场方面已显示出有希望的结果。然而，其在表观变化细微区域的准确性常会下降，且误差会随时间传播。先进的成像技术，如基于受激回波的位移编码（DENSE）CMR，能提供高精度且可重复的运动数据，但需要额外的图像采集，这在繁忙的临床工作流程中构成挑战。本文提出了一种新颖的潜在运动扩散模型（LaMoD），用于从标准CMR视频中预测高精度的DENSE运动。具体而言，我们的方法首先利用一个预训练配准网络的编码器，从图像序列中学习潜在运动特征（亦被视为基于形变的形状特征）。在DENSE提供的真实运动监督下，LaMoD随后利用概率潜在扩散模型从这些提取的特征中重建精确运动。实验结果表明，我们提出的方法LaMoD显著提高了标准CMR图像中运动分析的准确性，从而改善了临床环境下对心脏病患者的心肌应变分析。我们的代码将在论文被接受后公开。