发表机构
School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University（清华大学医学院生物医学工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
EchoDiST是一种结合自蒸馏联合学习与扩散条件网络的无监督框架，在三个超声心动图数据集上较七种方法显著提升了心肌运动估计及相关功能评估性能，可支持心脏功能定量评估。
AI 中文摘要
超声心动图中的运动估计对于心脏功能和心肌力学的定量评估至关重要，但由于图像伪影、图像信息有限、散斑去相关以及真实位移场的稀缺性，该任务仍具有挑战性。解剖学引导方法可提供结构信息，但通常依赖专家标注的心肌分割结果。我们提出EchoDiST，这是一种用于无监督超声心动图心肌运动估计的框架，它将基于自蒸馏的联合学习与扩散条件运动估计网络相结合。此处的无监督运动估计指的是在无真实位移场的情况下进行学习。自蒸馏策略在有限的解剖标注下联合优化解剖分割和心肌运动估计。训练过程中使用基于扩散的条件处理随机扰动，而推理仅需单次确定性前向传播，无需迭代反向扩散采样。在三个超声心动图数据集上对EchoDiST进行了评估，其中包括两个跨视图和跨数据集设置的外部测试数据集。与七种代表性的基于学习的方法相比，EchoDiST在解剖对齐、心肌应变评估以及运动衍生的功能和心脏阶段评估方面均实现了一致的提升。这些提升在所有评估任务和数据集上均具有统计学意义。总体而言，EchoDiST为在有限解剖监督下进行可靠的心肌运动估计提供了一种有效方法，并支持心脏功能的下游定量评估。
英文摘要
Motion estimation in echocardiography is essential for quantitative assessment of cardiac function and myocardial mechanics, but remains challenging due to image artifacts, limited image information, speckle decorrelation, and the scarcity of ground-truth displacement fields. Anatomy-guided approaches can provide structural information, yet often rely on expert-labeled myocardial segmentations. We propose EchoDiST, a framework for unsupervised echocardiographic myocardial motion estimation that integrates self-distillation-based joint learning with a diffusion-conditioned motion estimation network. Here, unsupervised motion estimation refers to learning without ground-truth displacement fields. The self-distillation strategy jointly optimizes anatomical segmentation and myocardial motion estimation under limited anatomical annotations. Diffusion-based conditioning is used during training with stochastic perturbations, while inference requires only a single deterministic forward pass without iterative reverse-diffusion sampling. EchoDiST was evaluated on three echocardiographic datasets, including two external test datasets under cross-view and cross-dataset settings. Compared with seven representative learning-based methods, EchoDiST consistently improved anatomical alignment, myocardial strain assessment, and motion-derived functional and cardiac-phase assessment. These gains were statistically significant across the evaluated tasks and datasets. Overall, EchoDiST provides an effective approach for reliable myocardial motion estimation under limited anatomical supervision and supports downstream quantitative assessment of cardiac function.
Comments19 pages, 14 figures
