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Proceedings Paper

Reduced memory augmented Lagrangian algorithm for 3D iterative x-ray CT image reconstruction
Author(s): Madison G. McGaffin; Sathish Ramani; Jeffrey A. Fessler
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Paper Abstract

Although statistical image reconstruction methods for X-ray CT can provide improved image quality at reduced patient doses, computation times for 3D axial and helical CT are a challenge. Rapidly converging algorithms are needed for practical use. Augmented Lagrangian methods based on variable splitting recently have been found to be effective for image denoising and deblurring applications.5 These methods are particularly effective for non-smooth regularizers such as total variation or those involving the 1 norm. However, when standard "split Bregman" methods6 are applied directly to 3D X-ray CT problems, numerous auxiliary variables are needed, leading to undesirably high memory requirements.7 For minimizing regularized, weighted least-squares (WLS) cost functions, we propose a new splitting approach for CT, based on the alternating direction method of multipliers (ADMM)1,5 that has multiple benefits over previous methods: (i) reduced memory requirements, (ii) effective preconditioning using modified ramp/cone filters, (iii) accommodating very general regularizers including edge-preserving roughness penalties, total variation methods, and sparsifying transforms like wavelets. Numerical results show that the proposed algorithm converges rapidly, and that the cone filter is particularly effective for accelerating convergence.

Paper Details

Date Published: 3 March 2012
PDF: 6 pages
Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 831327 (3 March 2012); doi: 10.1117/12.910941
Show Author Affiliations
Madison G. McGaffin, Univ. of Michigan (United States)
Sathish Ramani, Univ. of Michigan (United States)
Jeffrey A. Fessler, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 8313:
Medical Imaging 2012: Physics of Medical Imaging
Norbert J. Pelc; Robert M. Nishikawa; Bruce R. Whiting, Editor(s)

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