Transport-theory-based stochastic image reconstruction of bioluminescent sources.

A stochastic image reconstruction methodology is proposed for solving the highly ill-posed inverse bioluminescent source problem in light-scattering media. The unknown source distribution is expressed in terms of a set of linearly independent source basis functions. The bioluminescent boundary flux originating from each source basis function is computed prior to image reconstruction by solving the equation of radiative transfer. The misfit between the measured and the predicted boundary flux is described by an error function, which is iteratively minimized by stochastically sampling the global parameter space of all basis functions. Selection and alteration mechanisms, which can be guided by evolutionary principles found in nature, lead to new stochastic samples of source distributions for the next iteration cycle. A least-squares-error solution, representing the sought image of the unknown source distribution, is obtained after convergence. Numerical experiments demonstrate the feasibility of reconstructing bioluminescent source distributions in tissuelike media.