用真实超声的散斑退相干信息提升物理模拟真实性，改进心肌应变估计。
Deep Learning Strain Estimation: Is Physics-Based Simulation the Solution?
- 融合真实视频的散斑退相干特征，迭代优化物理模拟运动逼真度
- 在1478段视频上训练模型，全局应变变异系数降至1.42%（优于临床参考的1.78%）
- 适合心血管影像、深度学习与医学仿真交叉研究者参考
斑点追踪超声心动图（STE）是心肌应变评估的临床标准。尽管对全局应变（GLS）表现良好，其对局部应变的准确性仍受限，而该指标对早期诊断和细微异常表征至关重要。深度学习是潜在替代方案，但受限于缺乏可靠的运动标注。现有方法依赖于STE生成的标签或基于物理模型的仿真数据，但这些合成序列与真实临床数据相比仍缺乏真实感。本文提出一种新型仿真策略，引入真实视频中的散斑退相干测量值，并通过迭代精炼过程提升仿真运动的真实性。我们构建了一个开源的、包含1478段视频的高保真数据集，带有参考运动标注，并用于训练超声运动估计算法。所提方法在全局和局部应变估计上均达到最优性能，尤其在多专家对比中，全局应变变异系数低至1.42%，优于临床参考的1.78%。
原文摘要 · Abstract (English)
Speckle tracking echocardiography (STE) is the clinical standard for myocardial strain estimation. Despite good performance on global strain (GLS), its accuracy for regional strain remains limited, even though this biomarker is highly relevant for early diagnosis and the characterization of subtle abnormalities. from clinical data. Deep learning is a promising alternative, but its development is constrained by the lack of reliable motion references. Existing solutions rely either on STE-derived labels or on simulations generated by physics-based models, but these synthetic sequences still have limited realism compared with clinical data.In this paper, we propose a novel simulation strategy that incorporates speckle decorrelation measures from real videos and uses an iterative refinement process to improve the motion realism in the simulations. We created an open-source photorealistic dataset of 1,478 videos with reference motion, which was used to train an echocardiographic motion estimation algorithm. The proposed method achieves unmatched performance on global and regional strain, notably reaching a GLS variability of 1.42% in an inter-expert setting compared to 1.78% for the clinical reference.
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