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17 October 2022 Exploiting voxel-sparsity for bone imaging with sparse-view cone-beam computed tomography
Emil Y. Sidky, Holly L. Stewart, Christopher E. Kawcak, C. Wayne McIlwraith, Martine C. Duff, Xiaochuan Pan
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Proceedings Volume 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography; 123041Y (2022) https://doi.org/10.1117/12.2646892
Event: Seventh International Conference on Image Formation in X-Ray Computed Tomography (ICIFXCT 2022), 2022, Baltimore, United States
Abstract
An optimization-based image reconstruction framework is developed specifically for bone imaging. This framework exploits voxel-sparsity by use of ℓ1-norm image regularization and it enables image reconstruction from sparse-view cone-beam computed tomography (CBCT) acquisition. The effectiveness of the voxel-sparsity regularization is enhanced by using a blurred image representation. Ramp-filtering is included in the data discrepancy term and it has the effect of acting as a preconditioner, reducing the necessary number of iterations. The bone image reconstruction framework is demonstrated on CBCT data taken from an equine metacarpal condyle specimen.
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Emil Y. Sidky, Holly L. Stewart, Christopher E. Kawcak, C. Wayne McIlwraith, Martine C. Duff, and Xiaochuan Pan "Exploiting voxel-sparsity for bone imaging with sparse-view cone-beam computed tomography", Proc. SPIE 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography, 123041Y (17 October 2022); https://doi.org/10.1117/12.2646892
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KEYWORDS
Bone imaging
Image quality
Computed tomography
Reconstruction algorithms
Spatial resolution
X-ray imaging
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