Accelerating ordered subsets image reconstruction for X-ray CT using spatially nonuniform optimization transfer.

ABSTRACT: Statistical image reconstruction algorithms in X-ray computed tomography (CT) provide improved image quality for reduced dose levels but require substantial computation time. Iterative algorithms that converge in few iterations and that are amenable to massive parallelization are favorable in multiprocessor implementations. The separable quadratic surrogate (SQS) algorithm is desirable as it is simple and updates all voxels simultaneously. However, the standard SQS algorithm requires many iterations to converge. This paper proposes an extension of the SQS algorithm that leads to spatially nonuniform updates. The nonuniform (NU) SQS encourages larger step sizes for the voxels that are expected to change more between the current and the final image, accelerating convergence, while the deriva

SUBMITTER: Kim D 

PROVIDER: S-EPMC3818426 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature