Evolution strategies for optical tomographic characterization of homogeneous media

The reconstruction problem in diffuse optical tomography can be formulated as an optimization problem, in which an objective function has to be minimi zed. Current model-based iterative image reconstruction schemes commonly us e information about the gradient of the objective function to locate the mi nimum. These gradient-based search algorithms often find local minima close to an initial guess, or do not converge if the gradient is very small. If the initial guess is too far from the solution, gradient-based schemes prov e inefficient for finding the global minimum. In this work we introduce evo lution-strategy (ES) algorithms for diffuse optical tomography. These algor ithms seek to find the global minimum and are less sensitive to initial gue sses and regions with small gradients. We illustrate the fundamental concep ts by comparing the performance of gradient-based schemes and ES algorithms in finding optical properties (absorption coefficient mu (a), scattering c oefficient mu (s), and anisotropy factor g) of a homogenous medium. (C) 200 0 Optical Society of America.