Estimation of left ventricular parameters based on deep learning method

Li Cai; Jie Jiao; Pengfei Ma; Wenxian Xie; Yongheng Wang; Li Cai; Jie Jiao; Pengfei Ma; Wenxian Xie; Yongheng Wang

doi:10.3934/mbe.2022312

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 7: 6638-6658. doi: 10.3934/mbe.2022312

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Estimation of left ventricular parameters based on deep learning method

1.
Xi'an Key Laboratory of Scientific Computation and Applied Statistics, Xi'an 710129, China
2.
NPU-UoG International Cooperative Lab for Computation and Application in Cardiology, Xi'an 710129, China
3.
School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710129, China

Academic Editor: Alessandro Veneziani

Received: 04 December 2021 Revised: 09 April 2022 Accepted: 19 April 2022 Published: 27 April 2022

Estimating material properties of personalized human left ventricular (LV) modelling is a central problem in biomechanical studies. In this work we use deep learning (DL) method to evaluating the passive myocardial mechanical properties inversely. In the first part of the paper, we establish a standardized geometric model of the LV. The geometric model parameters are optimized based on 27 different healthy volunteers. In the second part, we use statistical methods and Latin hypercube sampling (LHS) to obtain the geometric parameters data. The LV myocardium is described using a structure-based orthotropic Holzapfel-Ogden constitutive law. The LV diastolic pressure-volume (PV) curves are calculated by numerical simulation. Tn the third part, we establish the multiple neural networks to pblackict PV curve parameters. Then, instead of using constrained optimization problems to solve constitutive parameters, DL was used to establish the nonlinear mapping relationship of geometric parameters, PV curve parameters and constitutive parameters. The results show that the deep learning method can greatly improve the computational efficiency of numerical simulation and increase the possibility of its application in rapid feedback of clinical data.
- left ventricular geometric parametric model,
- deep learning method based on neural network,
- inverse problem solving
Citation: Li Cai, Jie Jiao, Pengfei Ma, Wenxian Xie, Yongheng Wang. Estimation of left ventricular parameters based on deep learning method[J]. Mathematical Biosciences and Engineering, 2022, 19(7): 6638-6658. doi: 10.3934/mbe.2022312

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Abstract

Estimating material properties of personalized human left ventricular (LV) modelling is a central problem in biomechanical studies. In this work we use deep learning (DL) method to evaluating the passive myocardial mechanical properties inversely. In the first part of the paper, we establish a standardized geometric model of the LV. The geometric model parameters are optimized based on 27 different healthy volunteers. In the second part, we use statistical methods and Latin hypercube sampling (LHS) to obtain the geometric parameters data. The LV myocardium is described using a structure-based orthotropic Holzapfel-Ogden constitutive law. The LV diastolic pressure-volume (PV) curves are calculated by numerical simulation. Tn the third part, we establish the multiple neural networks to pblackict PV curve parameters. Then, instead of using constrained optimization problems to solve constitutive parameters, DL was used to establish the nonlinear mapping relationship of geometric parameters, PV curve parameters and constitutive parameters. The results show that the deep learning method can greatly improve the computational efficiency of numerical simulation and increase the possibility of its application in rapid feedback of clinical data.

References

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