Cardiac VFM visualization and analysis based on YOLO deep learning model and modified 2D continuity equation.

Journal: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
PMID: 32417649

Abstract

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.

Authors

  • Zhemin Zhuang
    Engineering College, Shantou University, Shantou, Guangdong, China.
  • Guobao Liu
    Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Department of Electronic Engineering, Shantou University, Shantou, Guangdong, China.
  • Wanli Ding
    Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Department of Electronic Engineering, Shantou University, Shantou, Guangdong, China.
  • Alex Noel Joseph Raj
    Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Department of Electronic Engineering, Shantou University, Shantou, Guangdong, China.
  • Shunmin Qiu
    Department of Ultrasound, First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
  • Jingfeng Guo
    Research Centre, Shantou Institute of Ultrasonic Instruments Co., Ltd., China.
  • Ye Yuan
    School of Artificial Intelligence and Automation, MOE Key Lab of Intelligent Control and Image Processing, Huazhong University of Science and Technology, Wuhan 430074, China.

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