Three-dimensional Bayesian optical image reconstruction with domain decomposition

Most current efforts in near-infrared optical tomography are effectively li mited to two-dimensional reconstructions due to the computationally intensi ve nature of full three-dimensional (3-D) data inversion. Previously, we de scribed a new computationally efficient and statistically powerful inversio n method APPRIZE (automatic progressive parameter-reducing inverse zonation and estimation). The APPRIZE method computes minimum-variance estimates of parameter values there, spatially variant absorption due to a fluorescent contrast agent) and covariance, while simultaneously estimating the number of parameters needed as well as the size, shape, and location of the spatia l regions that correspond to those parameters. Estimates of measurement and model error are explicitly incorporated into the procedure and implicitly regularize the inversion in a physically based manner, The optimal estimati on of parameters is bounds-constrained, precluding infeasible values. In th is paper, the APPRIZE method for optical imaging is extended for applicatio n to arbitrarily large 3-D domains through the use of domain decomposition. The effect of subdomain size on the performance of the method is examined by assessing the sensitivity for identifying 112 randomly located single-vo xel heterogeneities in 58 3-D domains, Also investigated are the effects of unmodeled heterogeneity in background optical properties. The method is te sted on simulated frequency-domain photon migration measurements at 100 MHz in order to recover absorption maps owing to fluorescent contrast agent. T his study provides a new approach for computationally tractable 3-D optical tomography.