Neural network-based inverse model for diffuse reflectance spectroscopy.

ABSTRACT: In diffuse reflectance spectroscopy, the retrieval of the optical properties of a target requires the inversion of a measured reflectance spectrum. This is typically achieved through the use of forward models such as diffusion theory or Monte Carlo simulations, which are iteratively applied to optimize the solution for the optical parameters. In this paper, we propose a novel neural network-based approach for solving this inverse problem, and validate its performance using experimentally measured diffuse reflectance data from a previously reported phantom study. Our inverse model was developed from a neural network forward model that was pre-trained with data from Monte Carlo simulations. The neural network forward model then creates a lookup table to invert the diffuse reflectance to the

SUBMITTER: Lan Q 

PROVIDER: S-EPMC10545200 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature