Image reconstruction for cone-beam computed tomography using total p-variation plus Kullback–Leibler data divergence

doi:10.1088/1674-1056/26/7/078701

中国物理B ›› 2017, Vol. 26 ›› Issue (7): 78701-078701.doi: 10.1088/1674-1056/26/7/078701

• INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY • 上一篇下一篇

Image reconstruction for cone-beam computed tomography using total p-variation plus Kullback–Leibler data divergence

Ai-Long Cai(蔡爱龙), Lei Li(李磊), Lin-Yuan Wang(王林元), Bin Yan(闫镔), Zhi-Zhong Zheng(郑治中), Han-Ming Zhang(张瀚铭), Guo-En Hu(胡国恩)

National Digital Switching System Engineering & Technological Research Centre, Zhengzhou 450002, China

收稿日期:2016-12-19 修回日期:2017-03-07 出版日期:2017-07-05 发布日期:2017-07-05
通讯作者: Bin Yan E-mail:ybspace@hotmail.com
基金资助:
Project supported by the National Natural Science Foundation of China (Grant Nos.61372172 and 61601518).

Image reconstruction for cone-beam computed tomography using total p-variation plus Kullback–Leibler data divergence

Ai-Long Cai(蔡爱龙), Lei Li(李磊), Lin-Yuan Wang(王林元), Bin Yan(闫镔), Zhi-Zhong Zheng(郑治中), Han-Ming Zhang(张瀚铭), Guo-En Hu(胡国恩)

National Digital Switching System Engineering & Technological Research Centre, Zhengzhou 450002, China

Received:2016-12-19 Revised:2017-03-07 Online:2017-07-05 Published:2017-07-05
Contact: Bin Yan E-mail:ybspace@hotmail.com
Supported by:
Project supported by the National Natural Science Foundation of China (Grant Nos.61372172 and 61601518).

摘要/Abstract

摘要： Accurate reconstruction from a reduced data set is highly essential for computed tomography in fast and/or low dose imaging applications. Conventional total variation (TV)-based algorithms apply the L1 norm-based penalties, which are not as efficient as Lp(0 <p <1) quasi-norm-based penalties. TV with a p-th power-based norm can serve as a feasible alternative of the conventional TV, which is referred to as total p-variation (TpV). This paper proposes a TpV-based reconstruction model and develops an efficient algorithm. The total p-variation and Kullback–Leibler (KL) data divergence, which has better noise suppression capability compared with the often-used quadratic term, are combined to build the reconstruction model. The proposed algorithm is derived by the alternating direction method (ADM) which offers a stable, efficient, and easily coded implementation. We apply the proposed method in the reconstructions from very few views of projections (7 views evenly acquired within 180°). The images reconstructed by the new method show clearer edges and higher numerical accuracy than the conventional TV method. Both the simulations and real CT data experiments indicate that the proposed method may be promising for practical applications.

关键词: image reconstruction, total p-variation minimization, Kullback–, Leibler data divergence, p-shrinkage mapping

Abstract: Accurate reconstruction from a reduced data set is highly essential for computed tomography in fast and/or low dose imaging applications. Conventional total variation (TV)-based algorithms apply the L1 norm-based penalties, which are not as efficient as Lp(0 <p <1) quasi-norm-based penalties. TV with a p-th power-based norm can serve as a feasible alternative of the conventional TV, which is referred to as total p-variation (TpV). This paper proposes a TpV-based reconstruction model and develops an efficient algorithm. The total p-variation and Kullback–Leibler (KL) data divergence, which has better noise suppression capability compared with the often-used quadratic term, are combined to build the reconstruction model. The proposed algorithm is derived by the alternating direction method (ADM) which offers a stable, efficient, and easily coded implementation. We apply the proposed method in the reconstructions from very few views of projections (7 views evenly acquired within 180°). The images reconstructed by the new method show clearer edges and higher numerical accuracy than the conventional TV method. Both the simulations and real CT data experiments indicate that the proposed method may be promising for practical applications.

Key words: image reconstruction, total p-variation minimization, Kullback–Leibler data divergence, p-shrinkage mapping

中图分类号: (Reconstruction)

87.57.nf

87.59.bd (Computed radiography) 87.59.bf (Digital radiography) 87.85.Pq (Biomedical imaging)

蔡爱龙, 李磊, 王林元, 闫镔, 郑治中, 张瀚铭, 胡国恩. Image reconstruction for cone-beam computed tomography using total p-variation plus Kullback–Leibler data divergence[J]. 中国物理B, 2017, 26(7): 78701-078701.

Ai-Long Cai(蔡爱龙), Lei Li(李磊), Lin-Yuan Wang(王林元), Bin Yan(闫镔), Zhi-Zhong Zheng(郑治中), Han-Ming Zhang(张瀚铭), Guo-En Hu(胡国恩). Image reconstruction for cone-beam computed tomography using total p-variation plus Kullback–Leibler data divergence[J]. Chin. Phys. B, 2017, 26(7): 78701-078701.

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Image reconstruction for cone-beam computed tomography using total p-variation plus Kullback–Leibler data divergence

Image reconstruction for cone-beam computed tomography using total p-variation plus Kullback–Leibler data divergence

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