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Proceedings Paper

Application of a non-convex smooth hard threshold regularizer to sparse-view CT image reconstruction
Author(s): Sean Rose; Emil Y. Sidky; Xioachuan Pan
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Paper Abstract

In this work, we apply non-convex, sparsity exploiting regularization techniques to image reconstruction in computed tomography (CT).We modify the well-known total variation (TV) penalty to use a non-convex smooth hard threshold (SHT) penalty as opposed to the typical ℓ1 norm. The SHT penalty is different from the p <1 norms in that it is bounded above and has bounded gradient as its argument approaches the zero vector. We propose a re-weighting scheme utilizing the Chambolle-Pock (CP) algorithm in an attempt to solve a data-error constrained optimization problem utilizing the SHT penalty and call the resulting algorithm SHTCP. We then demonstrate the algorithm on sparse-view reconstruction of a simulated breast phantom with noiseless and noisy data and compare the converged images to those generated by a CP algorithm solving the analogous data-error constrained problem utilizing the TV. We demonstrate that SHTCP allows for more accurate reconstruction in the case of sparse-view noisy data and, in the case of noiseless data, allows for accurate reconstruction from fewer views than its TV counterpart.

Paper Details

Date Published: 18 March 2015
PDF: 5 pages
Proc. SPIE 9412, Medical Imaging 2015: Physics of Medical Imaging, 941206 (18 March 2015); doi: 10.1117/12.2082116
Show Author Affiliations
Sean Rose, The Univ. of Chicago (United States)
Emil Y. Sidky, The Univ. of Chicago (United States)
Xioachuan Pan, The Univ. of Chicago (United States)

Published in SPIE Proceedings Vol. 9412:
Medical Imaging 2015: Physics of Medical Imaging
Christoph Hoeschen; Despina Kontos, Editor(s)

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