Investigating molecular transport in the human brain from MRI with physics-informed neural networks.

ABSTRACT: In recent years, a plethora of methods combining neural networks and partial differential equations have been developed. A widely known example are physics-informed neural networks, which solve problems involving partial differential equations by training a neural network. We apply physics-informed neural networks and the finite element method to estimate the diffusion coefficient governing the long term spread of molecules in the human brain from magnetic resonance images. Synthetic testcases are created to demonstrate that the standard formulation of the physics-informed neural network faces challenges with noisy measurements in our application. Our numerical results demonstrate that the residual of the partial differential equation after training needs to be small for accurate parameter

SUBMITTER: Zapf B 

PROVIDER: S-EPMC9474534 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature