# Diffusive optical tomography in the Bayesian framework

## TL;DR

This paper investigates the relationship between inverse problems for the radiative transfer equation and the diffusion equation in optical tomography, proving convergence of their Bayesian posteriors in the strong scattering regime.

## Contribution

It develops a rigorous framework to compare inverse problems for RTE and DE within the Bayesian setting, demonstrating posterior convergence in the asymptotic regime.

## Key findings

- Proves convergence of RTE inverse problem to DE inverse problem in Bayesian framework.
- Uses Hellinger metric and Kullback-Leibler divergence to quantify posterior convergence.
- Establishes theoretical foundation for multiscale approximation in optical tomography.

## Abstract

Many naturally-occuring models in the sciences are well-approximated by simplified models, using multiscale techniques. In such settings it is natural to ask about the relationship between inverse problems defined by the original problem and by the multiscale approximation. We develop an approach to this problem and exemplify it in the context of optical tomographic imaging. Optical tomographic imaging is a technique for infering the properties of biological tissue via measurements of the incoming and outgoing light intensity; it may be used as a medical imaging methodology. Mathematically, light propagation is modeled by the radiative transfer equation (RTE), and optical tomography amounts to reconstructing the scattering and the absorption coefficients in the RTE from boundary measurements. We study this problem in the Bayesian framework, focussing on the strong scattering regime. In this regime the forward RTE is close to the diffusion equation (DE). We study the RTE in the asymptotic regime where the forward problem approaches the DE, and prove convergence of the inverse RTE to the inverse DE in both nonlinear and linear settings. Convergence is proved by studying the distance between the two posterior distributions using the Hellinger metric, and using Kullback-Leibler divergence.