经食管超声心动图中节段性纵向应变的深度学习估计
Estimation of Segmental Longitudinal Strain in Transesophageal Echocardiography by Deep Learning
AI总结：
本研究提出首个基于深度学习的经食管超声心动图自动化节段性纵向应变估计流水线autoStrain，通过比较TeeFlow和TeeTracker两种模型，结合合成数据训练与临床验证，显著提升了心脏功能评估的精度与效率。
摘要：
左心室（LV）的节段性纵向应变（SLS）是评估局部左心室功能障碍的重要预后指标，特别是对于心肌缺血的诊断和治疗。目前的应变估计技术需要大量的人工干预和专业知识，限制了其效率，使其对于监测目的而言过于资源密集。本研究引入了首个自动化流水线autoStrain，用于在经食管超声心动图（TEE）中使用深度学习（DL）方法进行运动估计以实现SLS估计。我们对两种DL方法进行了比较分析：基于RAFT光流模型进行密集逐帧预测的TeeFlow，以及基于CoTracker点轨迹模型进行稀疏长序列预测的TeeTracker。由于很难从真实的超声心动图序列中获取真实运动数据，我们利用独特的模拟流水线（SIMUS）生成了包含80名患者的高逼真度合成TEE（synTEE）数据集，并带有真实心肌运动数据，以训练和评估这两个模型。我们的评估表明，TeeTracker在准确性上优于TeeFlow，在synTEE测试数据集上的运动估计平均距离误差为0.65 mm。对16名患者的临床验证进一步表明，使用我们的autoStrain流水线进行的SLS估计与临床参考相一致，平均差异（95%一致性界限）为1.09%（-8.90%至11.09%）。在synTEE数据中加入模拟缺血提高了模型量化异常变形的准确性。我们的研究结果表明，将AI驱动的运动估计与TEE相结合，可以显著提高临床环境中心脏功能评估的精度和效率。
英文摘要：
Segmental longitudinal strain (SLS) of the left ventricle (LV) is an important prognostic indicator for evaluating regional LV dysfunction, in particular for diagnosing and managing myocardial ischemia. Current techniques for strain estimation require significant manual intervention and expertise, limiting their efficiency and making them too resource-intensive for monitoring purposes. This study introduces the first automated pipeline, autoStrain, for SLS estimation in transesophageal echocardiography (TEE) using deep learning (DL) methods for motion estimation. We present a comparative analysis of two DL approaches: TeeFlow, based on the RAFT optical flow model for dense frame-to-frame predictions, and TeeTracker, based on the CoTracker point trajectory model for sparse long-sequence predictions. As ground truth motion data from real echocardiographic sequences are hardly accessible, we took advantage of a unique simulation pipeline (SIMUS) to generate a highly realistic synthetic TEE (synTEE) dataset of 80 patients with ground truth myocardial motion to train and evaluate both models. Our evaluation shows that TeeTracker outperforms TeeFlow in accuracy, achieving a mean distance error in motion estimation of 0.65 mm on a synTEE test dataset. Clinical validation on 16 patients further demonstrated that SLS estimation with our autoStrain pipeline aligned with clinical references, achieving a mean difference (95\% limits of agreement) of 1.09% (-8.90% to 11.09%). Incorporation of simulated ischemia in the synTEE data improved the accuracy of the models in quantifying abnormal deformation. Our findings indicate that integrating AI-driven motion estimation with TEE can significantly enhance the precision and efficiency of cardiac function assessment in clinical settings.
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