Zhuang Zhemin, Liu Guobao, Ding Wanli, Raj Alex Noel Joseph, Qiu Shunmin, Guo Jingfeng, Yuan Ye
Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Department of Electronic Engineering, Shantou University, Shantou, Guangdong, China.
Imaging Department, First Hospital of Medical College of Shantou University, Shantou, Guangdong, China.
Comput Med Imaging Graph. 2020 Jun;82:101732. doi: 10.1016/j.compmedimag.2020.101732. Epub 2020 Apr 28.
In order to realize the visual analysis of cardiac fluid motion, according to the characteristics of cardiac flow field ultrasound image, a method for the cardiac Vector Flow Mapping (VFM) analysis and evaluation based on the You-Only-Look-Once (YOLO) deep learning model and the improved two-dimensional continuity equation is proposed in this paper. Firstly, based on the ultrasound Doppler data, the radial velocity values of the blood particles are obtained; due to the real-time VFM's high requirement on the computing speed, the YOLO deep learning model is combined with an improved block matching algorithm for the localization and tracking of myocardial wall, and then the azimuth velocity of myocardial wall speckles can be obtained; in addition, it is proposed in this paper to use a nonlinear weight function to fuse the radial velocity of the blood particles and azimuth velocity of myocardial wall speckles nonlinearly, and further the vortex streamline diagram in the cardiac flow field can be obtained. The results of the experiments on the evaluation of the Ultrasonic apical long-axis view show that the proposed method not only improves the accuracy of VFM, but also provides a new evaluation basis for cardiac function impairment.
为实现心脏流体运动的可视化分析，根据心脏流场超声图像的特点，本文提出了一种基于单阶段多框检测器（YOLO）深度学习模型和改进的二维连续性方程的心脏矢量血流图（VFM）分析与评估方法。首先，基于超声多普勒数据，获取血液颗粒的径向速度值；由于实时VFM对计算速度要求较高，将YOLO深度学习模型与改进的块匹配算法相结合，用于心肌壁的定位和跟踪，进而得到心肌壁散斑的方位速度；此外，本文提出使用非线性权重函数对血液颗粒的径向速度和心肌壁散斑的方位速度进行非线性融合，进一步得到心脏流场中的涡流线图。超声心尖长轴视图评估实验结果表明，该方法不仅提高了VFM的准确性，还为心功能损害提供了新的评估依据。
