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 64×64 image grids, with 96 view angles, the single-instruction, multiple data (SIMD) distributed array processor containing 64² 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