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Proceedings Paper

LBP-based penalized weighted least-squares approach to low-dose cone-beam computed tomography reconstruction
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Paper Abstract

Cone-beam computed tomography (CBCT) has attracted growing interest of researchers in image reconstruction. The mAs level of the X-ray tube current, in practical application of CBCT, is mitigated in order to reduce the CBCT dose. The lowering of the X-ray tube current, however, results in the degradation of image quality. Thus, low-dose CBCT image reconstruction is in effect a noise problem. To acquire clinically acceptable quality of image, and keep the X-ray tube current as low as achievable in the meanwhile, some penalized weighted least-squares (PWLS)-based image reconstruction algorithms have been developed. One representative strategy in previous work is to model the prior information for solution regularization using an anisotropic penalty term. To enhance the edge preserving and noise suppressing in a finer scale, a novel algorithm combining the local binary pattern (LBP) with penalized weighted leastsquares (PWLS), called LBP-PWLS-based image reconstruction algorithm, is proposed in this work. The proposed LBP-PWLS-based algorithm adaptively encourages strong diffusion on the local spot/flat region around a voxel and less diffusion on edge/corner ones by adjusting the penalty for cost function, after the LBP is utilized to detect the region around the voxel as spot, flat and edge ones. The LBP-PWLS-based reconstruction algorithm was evaluated using the sinogram data acquired by a clinical CT scanner from the CatPhan® 600 phantom. Experimental results on the noiseresolution tradeoff measurement and other quantitative measurements demonstrated its feasibility and effectiveness in edge preserving and noise suppressing in comparison with a previous PWLS reconstruction algorithm.

Paper Details

Date Published: 19 March 2014
PDF: 8 pages
Proc. SPIE 9033, Medical Imaging 2014: Physics of Medical Imaging, 903336 (19 March 2014); doi: 10.1117/12.2043289
Show Author Affiliations
Ming Ma, Stony Brook Univ. (United States)
Huafeng Wang, Stony Brook Univ. (United States)
Yan Liu, Stony Brook Univ. (United States)
Hao Zhang, Stony Brook Univ. (United States)
Xianfeng Gu, Stony Brook Univ. (United States)
Zhengrong Liang, Stony Brook Univ. (United States)

Published in SPIE Proceedings Vol. 9033:
Medical Imaging 2014: Physics of Medical Imaging
Bruce R. Whiting; Christoph Hoeschen, Editor(s)

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