Abstract
The regularization plays an important role in the sparse-view x-ray computer tomography (CT) reconstruction. Based on the piecewise constant assumption, total variation (TV) regularization has been widely discussed for the sparse-view CT reconstruction. However, TV minimization often leads to some loss of the image edge information during reducing the image noise and artifacts. To overcome the drawback of TV regularization, this paper proposes to introduce a novel Mumford-Shah total variation (MSTV) regularization by integrating TV minimization and Mumford–Shah segmentation. Subsequently, a penalized weighted least-squares (PWLS) scheme with MSTV is presented for the sparse-view CT reconstruction. To evaluate the performance of our PWLS-MSTV algorithm, both qualitative and quantitative analyses are executed via phantom experiments. Experimental results show that the proposed PWLS-MSTV algorithm can attain notable gains in terms of accuracy and resolution properties over the TV regularization based algorithm.
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Acknowledgements
This work is partially supported by the National Natural Science Foundation of China (61272252, 81371544), the National Science Technology Major Project of the Ministry of Science and Technology of China (2014BAI17B02) and Science and Technology Planning Project of Shenzhen City (JCYJ20140828163633997, JCYJ20130326111024546). The authors also acknowledge the Key Laboratory of Medical Image Processing in Southern Medical University for providing the experimental data. Additionally, the authors also acknowledge Bar Leach for sharing their work.
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Chen, B. et al. (2015). Sparse-View X-ray Computed Tomography Reconstruction via Mumford-Shah Total Variation Regularization. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_79
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DOI: https://doi.org/10.1007/978-3-319-22053-6_79
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