Bone sparsity model for computed tomography image reconstruction

Emil Y. Sidky; Holly L. Stewart; Christopher E. Kawcak; C. Wayne McIlwraith; Martine C. Duff; Xiaochuan Pan

doi:10.1117/12.2534947

28 May 2019 Bone sparsity model for computed tomography image reconstruction

Emil Y. Sidky, Holly L. Stewart, Christopher E. Kawcak, C. Wayne McIlwraith, Martine C. Duff, Xiaochuan Pan

Author Affiliations +

Proceedings Volume 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine; 110721U (2019) https://doi.org/10.1117/12.2534947
Event: Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, Philadelphia, United States

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.

Citation Download Citation

Emil Y. Sidky, Holly L. Stewart, Christopher E. Kawcak, C. Wayne McIlwraith, Martine C. Duff, and Xiaochuan Pan "Bone sparsity model for computed tomography image reconstruction", Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 110721U (28 May 2019); https://doi.org/10.1117/12.2534947

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