Shape-constrained reconstruction in diffuse optical tomography by simulated annealing

TL;DR

This paper introduces a method for shape-constrained diffuse optical tomography reconstruction using simulated annealing to effectively solve the nonlinear inverse problem and avoid local minima.

Contribution

It proposes a novel approach combining shape constraints with simulated annealing for improved DOT reconstruction accuracy.

Findings

Shape constraints reduce unknown parameters in DOT.

Simulated annealing effectively avoids local minima.

Nonlinear inverse problem is successfully solved.

Abstract

When the inverse problem of diffuse optical tomography (DOT) is solved with the Born or Rytov approximation, the size of the matrix of the linear inverse problem becomes large if the volume (or area) of the domain in biological tissue used for reconstruction is large. The number of unknown parameters in DOT is reduced when constraints about the shape of a target are imposed for the inverse problem. Due to such constraints, the inverse problem becomes nonlinear even when the (first) Born or Rytov approximation is employed. We solve this nonlinear inverse problem by the simulated annealing, which is not trapped by local minima of the cost function.