Approximate Marginalization of Absorption and Scattering in Fluorescence Diffuse Optical Tomography

TL;DR

This paper introduces a Bayesian approximation error method to improve fluorescence diffuse optical tomography (fDOT) by compensating for inaccuracies in optical property modeling, enhancing image reconstruction robustness.

Contribution

It applies the Bayesian approximation error approach to fDOT, addressing modeling errors due to uncertain absorption and scattering properties, which was not previously explored.

Findings

Bayesian approach improves robustness against optical property errors

Enhanced accuracy in fluorophore concentration reconstruction

Method validated with simulated data showing error compensation

Abstract

In fluorescence diffuse optical tomography (fDOT), the reconstruction of the fluorophore concentration inside the target body is usually carried out using a normalized Born approximation model where the measured fluorescent emission data is scaled by measured excitation data. One of the benefits of the model is that it can tolerate inaccuracy in the absorption and scattering distributions that are used in the construction of the forward model to some extent. In this paper, we employ the recently proposed Bayesian approximation error approach to fDOT for compensating for the modeling errors caused by the inaccurately known optical properties of the target in combination with the normalized Born approximation model. The approach is evaluated using a simulated test case with different amount of error in the optical properties. The results show that the Bayesian approximation error approach improves the tolerance of fDOT imaging against modeling errors caused by inaccurately known absorption and scattering of the target.