Three-dimensional Bayesian optical diffusion tomography with experimental data

Abstract

Reconstructions of a three-dimensional absorber embedded in a scattering medium by use of frequency domain measurements of the transmitted light in a single source-detector plane are presented. The reconstruction algorithm uses Bayesian regularization and iterative coordinate descent optimization, and it incorporates estimation of the detector noise level, the source-detector coupling coefficient, and the background diffusion coefficient in addition to the absorption image. The use of multiple modulation frequencies is also investigated. The results demonstrate the utility of this algorithm, the importance of a three-dimensional model, and that out-of-plane scattering permits recovery of three-dimensional features from measurements in a single plane.