Three-dimensional optical tomography

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 (i) synthetic frequency-domain measurements; (ii) finite difference solution to the diffusion equation employing partial current boundary conditions; (iii) a recursive, minimum variance, optimization algorithm employing a Bayesian approximate extended Kalman filter accounting for measurement and system noise; and (iv) a unique, data-driven zonation scheme to dynamically determine parameterization and accelerate convergence. Using a synthetic data set with 0.1° standard deviation Gaussian noise added to phase, we demonstrate the ability to image multiple distinct 0.5 cm diameter absorbing/fluorescing heterogeneities within a combined transillumination/reflectance geometry comprising 8 sources and 90 detectors. 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.