Biomedical optical tomography using dynamic parameterization and Bayesian conditioning on photon migration measurements

Stochastic reconstruction techniques are developed for mapping the interior optical properties of tissues from exterior frequency-domain photon migrat ion measurements at the air-tissue interface. Parameter fields of absorptio n cross section, fluorescence lifetime, and quantum efficiency are accurate ly reconstructed from simulated noisy measurements of phase shift and ampli tude modulation by use of a recursive, Bayesian, minimum-variance estimator known as the approximate extended Kalman filter. Parameter field updates a re followed by data-driven zonation to improve the accuracy, stability, and computational efficiency of the method by moving the system from an underd etermined toward an overdetermined set of equations. These methods were ori ginally developed by Eppstein and Dougherty [Water Resources Res. 32, 3321 (1996)] for applications in geohydrology. Estimates are constrained to with in feasible ranges by modeling of parameters as beta-distributed random var iables. No arbitrary smoothing, regularization, or interpolation is require d. Results are compared with those determined by use of Newton-Raphson-base d inversions. The speed and accuracy of these preliminary Bayesian reconstr uctions suggest the near-future application of this inversion technology to three-dimensional biomedical imaging with frequency-domain photon migratio n. (C) 1999 Optical Society of America.