Parallel approach to iterative tomographic reconstruction for high resolution PET imaging

The authors present a parallel approach to iterative reconstruction algorithms for high resolution 2-D and 3-D PET imaging on a multiprocessor machine where processors are connected by fast Ethernet connections. An efficient parallel implementation of the ML-EM and OS-EM approaches is formulated. It makes use of pre-calculated transition matrices, utilizing the combination of four techniques for storage reduction: (1) a sparse matrix approach, storing only non-negligible values, (2) the elimination of all symmetries, (3) the elimination of matrix elements outside the aperture of the tomograph and (4) the partition of the transition matrix amongst the different processors. This approach can be used in practice for very large 2-D images, as it reduces the storage size of transition matrices per processor by a factor of 8200 for images of 64/spl times/64 up to a factor of 98000 for images of 1024/spl times/1024. A performance analysis of the parallel algorithm is presented. The parallel ML-EM approach with a pre-computed transition matrix can reconstruct 128/spl times/128 pixels images in 0.25 sec/iteration, as opposed to several minutes when the matrix is calculated on the fly by a single processor. Some parametrisations of the parallel OS-FM approach offer total reconstruction times of 2 seconds for 128/spl times/128 pixels images.