Translator Disclaimer
28 May 2019 Bone sparsity model for computed tomography image reconstruction
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.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
PROCEEDINGS
5 PAGES


SHARE
Advertisement
Advertisement
Back to Top