Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 36, Issue 12, Pages 2487-2498Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2017.2767290
Keywords
Computed tomography; noise modeling; maximum a posteriori (MAP); statistical model; regularization
Categories
Funding
- National Natural Science Foundation of China [61373114, 61661166011, 11690011, 61603292, 61721002, 81371544, 61571214]
- National Grand Fundamental Research 973 Program of China [2013CB329404]
- Science and Technology Program of Guangdong, China [2015B020233008]
- Science and Technology Program of Guangzhou, China [201510010039]
- Guangdong Natural Science Foundation [2015A030313271]
- National Institutes of Health [R01CA206171]
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Computed tomography (CT) image recovery from low-mAs acquisitions without adequate treatment is always severely degraded due to a number of physical factors. In this paper, we formulate the low-dose CT sinogram preprocessing as a standard maximum a posteriori (MAP) estimation, which takes full consideration of the statistical properties of the two intrinsic noise sources in low-dose CT, i.e., the X-ray photon statistics and the electronic noise background. In addition, instead of using a general image prior as found in the traditional sinogram recovery models, we design a new prior formulation to more rationally encode the piecewise-linear configurations underlying a sinogram than previously used ones, like the TV prior term. As compared with the previous methods, especially the MAP-based ones, both the likelihood/loss and prior/regularization terms in the proposed model are ameliorated in a more accurate manner and better comply with the statistical essence of the generation mechanism of a practical sinogram. We further construct an efficient alternating direction method of multipliers algorithm to solve the proposed MAP framework. Experiments on simulated and real low-dose CT data demonstrate the superiority of the proposed method according to both visual inspection and comprehensive quantitative performance evaluation.
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