Reconstruction of optical parameters for molecular tomographic imaging

TL;DR

This paper introduces a hybrid optimization algorithm combining global and gradient-based methods to accurately reconstruct tissue optical parameters in molecular tomographic imaging, overcoming local minima issues.

Contribution

A novel hybrid optimization approach using differential evolution and gradient methods for improved optical parameter reconstruction in molecular imaging.

Findings

The method is stable and accurate in numerical simulations.

It eliminates the need for initial guesses of optical parameters.

The approach effectively avoids local minima in optimization.

Abstract

Optical molecular tomographic imaging is to reconstruct the concentration distribution of photon-molecular probes in a small animal from measured photon fluence rates. The localization and quantification of molecular probes is related to tissue optical properties. The optical parameters can be reconstructed by the optimization method, in which the objective function is not convex, and there are multiple local optimal solutions. The gradient-based optimization algorithm could converge prematurely to an inaccurate solution if the initial guess is not close enough to the true solution. In this paper, we develop a hybrid algorithm combined global optimization and gradient-based optimization methods to reconstruct optical parameters. Differential evolution is an effective global optimizer, and could yield an estimated solution near the global optimal region to eliminate the need of initial guess of the optical parameters. The gradient-based optimization method is further performed with initial values obtained from DE to obtain local optimal solution for more accurate reconstruction of optical parameters. This proposed reconstruction method is evaluated by comprehensive numerical simulation experiments. The results show that the proposed method is stable and accurate for the reconstruction of optical parameters.