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Research on inversion method of left ventricular myocardial tissue parameters based on BP neural network
Vol 2, Issue 1, 2021
Issue release: 30 June 2021
VIEWS - 2952 (Abstract)
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Abstract
Because it contains pathological characteristics such as changes in myocardial tissue characteristics, the deformation and dynamic characteristics of human left ventricle have become an important basis for clinical diagnosis of heart disease. Based on BP neural network method, this study carries out the identification of left ventricular myocardial tissue parameters through the inversion of left ventricular clinical diagnosis data. Firstly, the image recognition program is written in MATLAB language to extract the location points of inner and outer membranes in human left ventricular CT image, establish the real geometric model of left ventricle in solidworks software, and establish the finite element analysis model of left ventricle through ABAQUS software. Secondly, Mooney Rivlin hyperelastic model is used to simulate the characteristics of myocardial tissue, ABAQUS finite element software is used to conduct dynamic numerical analysis on the left ventricular finite element model, and 45 groups of input target vectors of BP neural network corresponding to three characteristic moments are obtained. Finally, the BP neural network program is written in MATLAB language to train the input target vector, and establish the nonlinear mapping relationship between left ventricular diagnostic data and myocardial tissue parameters. The analysis results of examples show that BP neural network can be well used for myocardial tissue parameter inversion based on clinical data, and is expected to become an effective method for clinical diagnosis of left ventricular lesions caused by changes in myocardial tissue characteristics.
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References
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Copyright (c) 2021 Qishuai Zhang, Kehu Yang, Luxian Li
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Prof. Prakash Deedwania
University of California,
San Francisco, United States
