Angle estimation is an important step in the Doppler ultrasound clinical workflow to measure blood velocity. It is widely recognized that incorrect angle estimation is a leading cause of error in Doppler-based blood velocity measurements. In this paper, we propose a deep learning-based approach for automated Doppler angle estimation. The approach was developed using 2100 human carotid ultrasound images including image augmentation. Five pre-trained models were used to extract images features, and these features were passed to a custom shallow network for Doppler angle estimation. Independently, measurements were obtained by a human observer reviewing the images for comparison. The mean absolute error (MAE) between the automated and manual angle estimates ranged from 3.9° to 9.4° for the models evaluated. Furthermore, the MAE for the best performing model was less than the acceptable clinical Doppler angle error threshold thus avoiding misclassification of normal velocity values as a stenosis. The results demonstrate potential for applying a deep-learning based technique for automated ultrasound Doppler angle estimation. Such a technique could potentially be implemented within the imaging software on commercial ultrasound scanners.

翻译：角度估计是临床多普勒超声测量血流速度工作流程中的重要步骤。业界普遍认为，角度估计不准确是导致基于多普勒的血流速度测量误差的主要原因。本文提出了一种基于深度学习的自动多普勒角度估计方法。该方法基于2100张人类颈动脉超声图像（含图像增强）进行开发。研究采用五种预训练模型提取图像特征，并将这些特征输入定制浅层网络进行多普勒角度估计。同时由人工观察者独立审阅图像进行对比测量。经评估，各模型的自动角度估计与人工角度估计之间的平均绝对误差（MAE）范围为3.9°至9.4°。其中，最佳性能模型的MAE低于临床可接受的多普勒角度误差阈值，从而避免了将正常流速值误判为狭窄。研究结果表明，将基于深度学习的技术应用于超声多普勒角度自动估计具有可行性。该技术有望集成至商用超声扫描仪的成像软件中。