Abstract

We present a tomography method for fluorescence and absorption biomedical optical imaging which minimizes the computational burden of three-dimensional image reconstruction and enables data conditioning on the basis of variable and possibly spatially-correlated measurement and system noise. Specifically, we present three-dimensional images reconstructed from synthetic frequency-domain measurements and a recursive, minimum variance, optimization algorithm employing a Bayesian approximate extended Kalman filter accounting for measurement and system noise with a unique, data- driven zonation scheme to dynamically determine parameterization and accelerate convergence. Reconstruction of absorption maps owing to spatial distribution of fluorophores that were discretized onto a 9×9×9 node grid required just over 4 minutes on a 350 MHz Pentium II computer.

© 1999 Optical Society of America