Publication: Personalising left-ventricular biophysical models of the heart using parametric physics-informed neural networks
Personalising left-ventricular biophysical models of the heart using parametric physics-informed neural networks
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Buoso, S., Joyce, T., & Kozerke, S. (2021). Personalising left-ventricular biophysical models of the heart using parametric physics-informed neural networks. Medical Image Analysis, 71, 102066. https://doi.org/10.1016/j.media.2021.102066
Abstract
Abstract
Abstract
We present a parametric physics-informed neural network for the simulation of personalised left-ventricular biomechanics. The neural network is constrained to the biophysical problem in two ways: (i) the network output is restricted to a subspace built from radial basis functions capturing characteristic deformations of left ventricles and (ii) the cost function used for training is the energy potential functional specifically tailored for hyperelastic, anisotropic, nearly-incompressible active materials. The radial bases are generate
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- Buoso, Stefano
- Joyce, Thomas
- Kozerke, Sebastian
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Citations
Buoso, S., Joyce, T., & Kozerke, S. (2021). Personalising left-ventricular biophysical models of the heart using parametric physics-informed neural networks. Medical Image Analysis, 71, 102066. https://doi.org/10.1016/j.media.2021.102066
