Accelerating Ordered Subsets Image Reconstruction for X-ray CT Using Spatially Nonuniform Optimization Transfer

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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 derivation of NU-SQS guarantees monotonic descent. Ordered subsets (OS) algorithms can also accelerate SQS, provided suitable "subset balance" conditions hold. These conditions can fail in 3-D helical cone-beam CT due to incomplete sampling outside the axial region-of-interest (ROI). This paper proposes a modified OS algorithm that is more stable outside the ROI in helical CT. We use CT scans to demonstrate that the proposed NU-OS-SQS algorithm handles the helical geometry better than the conventional OS methods and "converges" in less than half the time of ordinary OS-SQS.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2013-11
Language
English
Article Type
Article
Keywords

COORDINATE ASCENT ALGORITHMS; LINEAR INVERSE PROBLEMS; TRANSMISSION TOMOGRAPHY; ITERATIVE RECONSTRUCTION; THRESHOLDING ALGORITHM; HELICAL CT; LIKELIHOOD; PROJECTIONS

Citation

IEEE TRANSACTIONS ON MEDICAL IMAGING, v.32, no.11, pp.1965 - 1978

ISSN
0278-0062
DOI
10.1109/TMI.2013.2266898
URI
http://hdl.handle.net/10203/245471
Appears in Collection
MA-Journal Papers(저널논문)
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