Parallel statistical image reconstruction for cone-beam x-ray CT on a shared memory computation platform.

J S Kole; F J Beekman

doi:10.1088/0031-9155/50/6/015

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PubMed · 15798321

Parallel statistical image reconstruction for cone-beam x-ray CT on a shared memory computation platform.

J S Kole·F J Beekman

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Abstract

Statistical reconstruction methods offer possibilities of improving image quality as compared to analytical methods, but current reconstruction times prohibit routine clinical applications. To reduce reconstruction times we have parallelized a statistical reconstruction algorithm for cone-beam x-ray CT, the ordered subset convex algorithm (OSC), and evaluated it on a shared memory computer. Two different parallelization strategies were developed: one that employs parallelism by computing the work for all projections within a subset in parallel, and one that divides the total volume into parts and processes the work for each sub-volume in parallel. Both methods are used to reconstruct a three-dimensional mathematical phantom on two different grid densities. The reconstructed images are binary identical to the result of the serial (non-parallelized) algorithm. The speed-up factor equals approximately 30 when using 32 to 40 processors, and scales almost linearly with the number of cpus for both methods. The huge reduction in computation time allows us to apply statistical reconstruction to clinically relevant studies for the first time.

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Algorithms Artificial Intelligence Computer Simulation Computing Methodologies Imaging, Three-Dimensional Information Storage and Retrieval Models, Biological Models, Statistical Radiographic Image Enhancement Radiographic Image Interpretation, Computer-Assisted Reproducibility of Results Sensitivity and Specificity Tomography, X-Ray Computed Comparative Study Evaluation Study Journal Article Research Support, Non-U.S. Gov't Validation Study

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J S Kole, F J Beekman. 2005-03-02. Parallel statistical image reconstruction for cone-beam x-ray CT on a shared memory computation platform.. https://doi.org/10.1088/0031-9155%2F50%2F6%2F015

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