多普勒超声心动图综合分析的统一方法：涵盖频谱与组织多普勒
A Unified Approach for Comprehensive Analysis of Various Spectral and Tissue Doppler Echocardiography
AI总结：
提出一种基于卷积神经网络的统一框架，整合多普勒超声心动图的自动测量与舒张末期检测，通过形状嵌入和抗混叠模块提升性能，在DSC和IoU指标上优于现有方法。
摘要：
多普勒超声心动图通过量化血流速度和评估心肌运动，为心脏功能和时相提供关键见解。然而，以往用于自动化多普勒分析的方法，从最初的信号处理技术到先进的深度学习方法，均受限于对心电图（ECG）数据的依赖，且无法对多普勒视图进行整体处理。我们提出了一种新颖的统一框架，利用卷积神经网络对频谱和组织多普勒超声心动图图像进行全面分析，将自动测量和舒张末期（ED）检测整合为单一方法。该网络能自动识别不同多普勒视图中的关键特征，并通过新颖的多普勒形状嵌入和抗混叠模块增强图像解读，确保分析的一致性。实验结果表明，在包括Dice相似系数（DSC）和交并比（IoU）在内的性能指标上，该方法持续表现优异。所提出的框架在多普勒自动测量方面与临床医生表现出高度一致性，并在舒张末期检测方面具有竞争力的性能。
英文摘要：
Doppler echocardiography offers critical insights into cardiac function and phases by quantifying blood flow velocities and evaluating myocardial motion. However, previous methods for automating Doppler analysis, ranging from initial signal processing techniques to advanced deep learning approaches, have been constrained by their reliance on electrocardiogram (ECG) data and their inability to process Doppler views collectively. We introduce a novel unified framework using a convolutional neural network for comprehensive analysis of spectral and tissue Doppler echocardiography images that combines automatic measurements and end-diastole (ED) detection into a singular method. The network automatically recognizes key features across various Doppler views, with novel Doppler shape embedding and anti-aliasing modules enhancing interpretation and ensuring consistent analysis. Empirical results indicate a consistent outperformance in performance metrics, including dice similarity coefficients (DSC) and intersection over union (IoU). The proposed framework demonstrates strong agreement with clinicians in Doppler automatic measurements and competitive performance in ED detection.
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