Gaussian Distributed Semi-Analytic Reconstruction Method for Diffuse Optical Tomographic Measurement

A novel non-linear semi-analytic reconstruction method for continuous-wave diffuse optical tomography (CW-DOT) that employs a Gaussian distribution framework for tracing photon paths is presented here in this work. In highly scattering media-like tissue, photons travel on a curved photon path with cloud distribution between any light source–detector pairs. In this work, Gaussian distributed Rosenbrock’s banana function was utilized to trace the majority of photons between any source–detector pairs as curved paths with photon cloud distribution to mimic the actual photon distributions. This has led to a more realistic semi-analytic diffuse optical tomographic reconstruction technique and helped to improve the reconstruction accuracy significantly. This improved method is experimentally validated on multiple inclusion wax phantom and in vivo finger joint imaging of a healthy volunteer. Also, the proposed reconstruction method can directly estimate the tissue oxygenation level measured in terms of oxygen saturation (StO2) and oxygen capacity (
${C}_{B}$
) in vivo derived from hemoglobin concentrations using CW-DOT. Tissue oxygenation levels and oxygen capacities were estimated consistently and accurately. The results showed significant improvement in accuracy when compared to the generalized method where the photon paths are modeled as curved lines, estimated by different performance evaluation matrices.