PARALLELIZATION OF THE EM ALGORITHM FOR 3-D PET IMAGE-RECONSTRUCTION

The EM algorithm for the PET image reconstruction has several attactive advantages over the conventional filtered backprojection algorithms. However, two major drawbacks have impeded the routine use of the EM algorithm, namely, the long computation time due to slow convergence and a large memory required for the image, projection, and probability matrix. In this study, we attempt to solve these two problems by parallelizing the EM algorithm on multiprocessor systems. An efficient data and task partitioning scheme, called partition-by-box, based on the message passing model is proposed. The partition-by-box scheme and its modified version have been implemented on a message passing system, Intel iPSC/2, and a shared memory system BBN Butterfly GP1000. The implementation results show that, for the partition-by-box scheme, a message passing system of complete binary tree interconnection with fixed connectivity of three at each node can have similar performance as that with the hypercube topology which has connectivity of log 2 N for N PE's. It is shown that the EM algorithm can be efficiently parallelized using the (modified) partiton-by-box scheme with the message passing model.