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Proceedings Paper

Sampling conditions for gradient-magnitude sparsity based image reconstruction algorithms
Author(s): Emil Y. Sidky; Jakob H. Jørgensen; Xiaochuan Pan
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Paper Abstract

Image reconstruction from sparse-view data in 2D fan-beam CT is investigated by constrained, total-variation minimization. This optimization problem exploits possible sparsity in the gradient magnitude image (GMI). The investigation is performed in simulation under ideal, noiseless data conditions in order to reveal a possible link between GMI sparsity and the necessary number of projection views for reconstructing an accurate image. Results are shown for two, quite different phantoms of similar GMI sparsity.

Paper Details

Date Published: 3 March 2012
PDF: 8 pages
Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 831337 (3 March 2012); doi: 10.1117/12.913307
Show Author Affiliations
Emil Y. Sidky, The Univ. of Chicago (United States)
Jakob H. Jørgensen, Technical Univ. of Denmark (Denmark)
Xiaochuan Pan, The Univ. of Chicago (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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