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Combining Ordered Subsets and Momentum for Accelerated X-Ray CT Image Reconstruction | IEEE Journals & Magazine | IEEE Xplore

Combining Ordered Subsets and Momentum for Accelerated X-Ray CT Image Reconstruction


Abstract:

Statistical X-ray computed tomography (CT) reconstruction can improve image quality from reduced dose scans, but requires very long computation time. Ordered subsets (OS)...Show More

Abstract:

Statistical X-ray computed tomography (CT) reconstruction can improve image quality from reduced dose scans, but requires very long computation time. Ordered subsets (OS) methods have been widely used for research in X-ray CT statistical image reconstruction (and are used in clinical PET and SPECT reconstruction). In particular, OS methods based on separable quadratic surrogates (OS-SQS) are massively parallelizable and are well suited to modern computing architectures, but the number of iterations required for convergence should be reduced for better practical use. This paper introduces OS-SQS-momentum algorithms that combine Nesterov's momentum techniques with OS-SQS methods, greatly improving convergence speed in early iterations. If the number of subsets is too large, the OS-SQS-momentum methods can be unstable, so we propose diminishing step sizes that stabilize the method while preserving the very fast convergence behavior. Experiments with simulated and real 3D CT scan data illustrate the performance of the proposed algorithms.
Published in: IEEE Transactions on Medical Imaging ( Volume: 34, Issue: 1, January 2015)
Page(s): 167 - 178
Date of Publication: 22 August 2014

ISSN Information:

PubMed ID: 25163058

References

References is not available for this document.