Maximum likelihood SPECT in clinical computation times using mesh-connected parallel computers.

Extending the work of A.W. McCarthy et al. (1988) and M.I. Miller and B. Roysam (1991), the authors demonstrate that a fully parallel implementation of the maximum-likelihood method for single-photon emission computed tomography (SPECT) can be accomplished in clinical time frames on massively parallel systolic array processors. The authors show that for SPECT imaging on 64x64 image grids, with 96 view angles, the single-instruction, multiple data (SIMD) distributed array processor containing 64(2) processors performs the expectation-maximization (EM) algorithm with Good's smoothing at a rate of 1 iteration/1.5 s. This promises for emission tomography fully Bayesian reconstructions including regularization in clinical computation times which are on the order of 1 min/slice. The most important result of the implementations is that the scaling rules for computation times are roughly linear in the number of processors.