Optical imaging of phantoms from real data by an approximately globally convergent inverse algorithm

TL;DR

This paper introduces a numerical method for an inverse elliptic problem that achieves approximate global convergence without requiring a good initial guess, validated on real optical tomography data for brain stroke detection.

Contribution

The paper presents a novel inverse algorithm with approximate global convergence for optical imaging, applicable to real data in medical diagnostics.

Findings

Algorithm demonstrates approximate global convergence.

Validated on real Diffusion Optical Tomography data.

Potential application in stroke detection in small animals.

Abstract

A numerical method for an inverse problem for an elliptic equation with the running source at multiple positions is presented. This algorithm does not rely on a good first guess for the solution. The so-called "approximate global convergence" property of this method is shown here. The performance of the algorithm is verified on real data for Diffusion Optical Tomography. Direct applications are in near-infrared laser imaging technology for stroke detection in brains of small animals.