  Inverse boundary value problems for the radiative transport equation play
important roles in optics-based medical imaging techniques such as diffuse
optical tomography (DOT) and fluorescence optical tomography (FOT). Despite the
rapid progress in the mathematical theory and numerical computation of these
inverse problems in recent years, developing robust and efficient
reconstruction algorithms remains as a challenging task and an active research
topic. We propose here a robust reconstruction method that is based on subspace
minimization techniques. The method splits the unknown transport solution (or a
functional of it) into low-frequency and high-frequency components, and uses
singular value decomposition to analytically recover part of low-frequency
information. Minimization is then applied to recover part of the high-frequency
components of the unknowns. We present some numerical simulations with
synthetic data to demonstrate the performance of the proposed algorithm.
