Xing Jiarui, Jayakumar Nivetha, Wu Nian, Wang Yu, Epstein Frederick H, Zhang Miaomiao
Department of Electrical and Computer Engineering, University of Virginia, USA.
Department of Biomedical Engineering, University of Virginia Health System, USA.
Shape Med Imaging (2024). 2025;15275:164-177. doi: 10.1007/978-3-031-75291-9_13. Epub 2024 Oct 26.
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 changes, 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 is publicly available at https://github.com/jr-xing/LaMoD.
心脏磁共振成像（CMR）视频的运动和变形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序列预测运动场方面显示出了有前景的结果。然而，它们在外观变化细微的区域的准确性往往会降低，误差会随着时间传播。先进的成像技术，如刺激回波位移编码（DENSE）CMR，可提供高度准确和可重复的运动数据，但需要额外的图像采集，这在繁忙的临床流程中带来了挑战。在本文中，我们引入了一种新颖的潜在运动扩散模型（LaMoD），以从标准CMR视频预测高度准确的DENSE运动。更具体地说，我们的方法首先使用来自预训练配准网络的编码器，该编码器从图像序列中学习潜在运动特征（也被视为基于变形的形状特征）。在DENSE提供的真实运动的监督下，LaMoD然后利用概率潜在扩散模型从这些提取的特征中重建准确的运动。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；从而改善了心脏病患者临床环境中的心肌应变分析。我们的代码可在https://github.com/jr-xing/LaMoD上公开获取。
