Please use this identifier to cite or link to this item: http://dlib.scu.ac.ir/handle/Hannan/527444
Title: Robust Low-Dose CT Sinogram Preprocessing via Exploiting Noise-Generating Mechanism
Authors: Qi Xie;Dong Zeng;Qian Zhao;Deyu Meng;Zongben Xu;Zhengrong Liang;Jianhua Ma
Year: 2017
Publisher: IEEE
Abstract: 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.
URI: http://dl.kums.ac.ir/handle/Hannan/527444
volume: 36
issue: 12
More Information: 2487,
2498
Appears in Collections:2017

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Title: Robust Low-Dose CT Sinogram Preprocessing via Exploiting Noise-Generating Mechanism
Authors: Qi Xie;Dong Zeng;Qian Zhao;Deyu Meng;Zongben Xu;Zhengrong Liang;Jianhua Ma
Year: 2017
Publisher: IEEE
Abstract: 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.
URI: http://dl.kums.ac.ir/handle/Hannan/527444
volume: 36
issue: 12
More Information: 2487,
2498
Appears in Collections:2017

Files in This Item:
File Description SizeFormat 
8086204.pdf6.72 MBAdobe PDFThumbnail
Preview File
Title: Robust Low-Dose CT Sinogram Preprocessing via Exploiting Noise-Generating Mechanism
Authors: Qi Xie;Dong Zeng;Qian Zhao;Deyu Meng;Zongben Xu;Zhengrong Liang;Jianhua Ma
Year: 2017
Publisher: IEEE
Abstract: 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.
URI: http://dl.kums.ac.ir/handle/Hannan/527444
volume: 36
issue: 12
More Information: 2487,
2498
Appears in Collections:2017

Files in This Item:
File Description SizeFormat 
8086204.pdf6.72 MBAdobe PDFThumbnail
Preview File