A generative model of hyperelastic strain energy density functions for multiple tissue brain deformation.

ABSTRACT:

Purpose

Estimation of brain deformation is crucial during neurosurgery. Whilst mechanical characterisation captures stress-strain relationships of tissue, biomechanical models are limited by experimental conditions. This results in variability reported in the literature. The aim of this work was to demonstrate a generative model of strain energy density functions can estimate the elastic properties of tissue using observed brain deformation.

Methods

For the generative model a Gaussian Process regression learns elastic potentials from 73 manuscripts. We evaluate the use of neo-Hookean, Mooney-Rivlin and 1-term Ogden meta-models to guarantee stability. Single and multiple tissue experiments validate the ability of our generative model to estimate tissue properties on a synthet

SUBMITTER: Granados A 

PROVIDER: S-EPMC7822772 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature