Parallel computation for positron emission tomography with reduced processor commuications

Positron Emission Tomography (PET) is a diagnostic technique used to study func-tionality of human organs. A maximum likelihood reconstruction algorithm for PET, introduced by Shepp and Vardi [32] and Lange and Carson [23], produces images that are statistically superior to other methods, but is an iterative method with high time complexity. Most efforts at parallelization have been for single instruction stream, mul-tiple data stream (SIMD) parallel computers. This research extends the mathematical development of the iterative reconstruction and presents new results in parallel multiple instruction stream, multiple data stream (MIMD) computation with reduced processor communications. We obtain alternative convergence results and new results for rate of convergence of the iterative reconstruction algorithm. We show that the algorithm can be parallelized with reduced communications and show empirically using both simulated and clinical data that the reduced communication algorithm produces reconstructions that are comparable to reconstructions computed with full communication.