Evolution-theory-based algorithm for optical diffusion tomography

In diffuse optical diffuse tomography (DOT) one attempts to reconstruct cross-sectional images of various body parts given data from near-infrared transmission measurements. The cross-sectional images display the spatial distribution of optical properties, such as the absorption coefficient μ_a, the scattering coefficient μ_s, or a combination thereof. Most of the currently employed imaging algorithms are model-based iterative image reconstruction (MOBIIR) schemes that employ information about the gradient of a suitably defined objective function with respect to the optical properties. In this approach the image reconstruction problem is considered as a nonlinear optimization problem, where the unknowns are the values of optical properties throughout the medium to be reconstructed. It is well known that gradient-based schemes are inefficient in areas where the gradient is close to zero. These schemes often get caught in local minima close to the starting point of the search and have problems finding the global minimum. To overcome this problem we propose to employ optimization algorithms that make use of evolution strategies. These schemes are in general much better suited to find global minima and may be a better choice for the image reconstruction problem in diffuse optical tomography.