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Bone sparsity model for computed tomography image reconstruction
Author(s): Emil Y. Sidky; Holly L. Stewart; Christopher E. Kawcak; C. Wayne McIlwraith; Martine C. Duff; Xiaochuan Pan
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Paper Abstract

Gradient sparsity regularization is an effective way to mitigate artifacts due to sparse-view sampling or data noise in computed tomography (CT) image reconstruction. The effectiveness of this type of regularization relies on the scanned object being approximately piecewise constant. Trabecular bone tissue is also technically piecewise constant, but the fine internal structure varies at a spatial scale that is smaller than the resolution of a typical CT scan; thus it is not clear what form of sparsity regularization is most effective for this type of tissue. In this conference submission, we develop a pixel-sparsity regularization model, which is observed to be effective at reducing streak artifacts due to sparse-view sampling and noise. Comparison with gradient sparsity regularization is also shown.

Paper Details

Date Published: 28 May 2019
PDF: 5 pages
Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 110721U (28 May 2019); doi: 10.1117/12.2534947
Show Author Affiliations
Emil Y. Sidky, The Univ. of Chicago (United States)
Holly L. Stewart, Colorado State Univ. (United States)
Christopher E. Kawcak, Colorado State Univ. (United States)
C. Wayne McIlwraith, Colorado State Univ. (United States)
Martine C. Duff, Savanah River National Lab. (United States)
Xiaochuan Pan, The Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 11072:
15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
Samuel Matej; Scott D. Metzler, Editor(s)

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