Diffuse optical tomography by simulated annealing via a spin Hamiltonian

TL;DR

This paper introduces a novel numerical method for diffuse optical tomography that employs simulated annealing with a spin Hamiltonian to effectively reconstruct optical parameters without requiring good initial guesses.

Contribution

The paper presents a new approach using simulated annealing and a spin Hamiltonian to solve the inverse problem in DOT, overcoming limitations of traditional iterative schemes.

Findings

SA successfully converges from random initial states

Targets in the medium are accurately reconstructed

Method works without good initial guesses

Abstract

Diffuse optical tomography (DOT) is an imaging modality which uses near-infrared light. Although iterative numerical schemes are commonly used for its inverse problem, correct solutions are not obtained unless good initial guesses are chosen. We propose a numerical scheme of DOT which works even when good initial guesses of optical parameters are not available. We use simulated annealing (SA) which is a method of the Markov-chain Monte Carlo. To implement SA for DOT, a spin Hamiltonian is introduced in the cost function, and the Metropolis algorithm or single-component Metropolis-Hastings algorithm is used. By numerical experiments, it is shown that an initial random spin configuration is brought to a converged configuration by SA and targets in the medium are reconstructed. The proposed numerical method solves the inverse problem for DOT by finding the ground state of a spin Hamiltonian with SA.