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Multicomputer algorithms for reconstruction and postprocessing

The increasing computational demands of medical imaging will exceed the capacity of standard microprocessors. For the most computationally intense problems, such as real-time scanning, parallel processing will be required. We evaluate the performance of a master-slave model of coarse-grained parallel processing on examples of reconstruction and postprocessing problems. We use a commercially available multicomputer system in configurations of from one through eight processors with distributed, shared memory. We examine a variety of 2D medical imaging problems ranging from pointwise operations, such as window-level, to global operations, such as 2D FFT. Parallel processing with the master-slave model is most efficient when data transfer among processors is minimized. This can be done by a combination of high-performance computer architecture and well-designed processing algorithms.