Elasticity imaging using physics-informed neural networks: Spatial discovery of elastic modulus and Poisson's ratio.

ABSTRACT: Elasticity imaging is a technique that discovers the spatial distribution of mechanical properties of tissue using deformation and force measurements under various loading conditions. Given the complexity of this discovery, most existing methods approximate only one material parameter while assuming homogeneous distributions for the others. We employ physics-informed neural networks (PINN) in linear elasticity problems to discover the space-dependent distribution of both elastic modulus (E) and Poisson's ratio (ν) simultaneously, using strain data, normal stress boundary conditions, and the governing physics. We validated our model on three examples. First, we experimentally loaded hydrogel samples with embedded stiff inclusions, representing tumorous tissue, and compared the approximation

SUBMITTER: Kamali A 

PROVIDER: S-EPMC9805508 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature