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Cite Details

Emil Y. Sidky, Rick Chartrand, Jakob S. Jørgensen and Xiaochuan Pan, "Nonconvex optimization for improved exploitation of gradient sparsity in CT image reconstruction", in Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2013

Abstract

A nonconvex optimization algorithm is developed, which exploits gradient magnitude image (GMI) sparsity for reduction in the projection view angle sampling rate. The algorithm shows greater potential for exploiting GMI sparsity than can be obtained by convex total variation (TV) based optimization. The nonconvex algorithm is demonstrated in simulation with ideal, noiseless data for a 2D fan-beam computed tomography (CT) configuration, and with noisy data for a 3D circular cone-beam CT configuration.

BibTeX Entry

@inproceedings{sidky-2013-nonconvex,
author = {Emil Y. Sidky and Rick Chartrand and Jakob S. J\{o}rgensen and Xiaochuan Pan},
title = {Nonconvex optimization for improved exploitation of gradient sparsity in CT image reconstruction},
year = {2013},
urlpdf = {http://math.lanl.gov/Research/Publications/Docs/sidky-2013-nonconvex.pdf},
booktitle = {Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine}
}