期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 38, 期 11, 页码 2607-2619出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2019.2906853
关键词
Image reconstruction; Computed tomography; Convolutional codes; Dictionaries; TV; Image coding; Compressed sensing; Computed tomography; compressed sensing CT reconstruction; convolutional sparse coding
类别
资金
- National Natural Science Foundation of China [61671312]
- Sichuan Science and Technology Program [2018HH0070]
- Miaozi Project in Science and Technology Innovation Program of Sichuan Province [18-YCG041]
Over the past few years, dictionary learning (DL)-based methods have been successfully used in various image reconstruction problems. However, the traditional DL-based computed tomography (CT) reconstruction methods are patch-based and ignore the consistency of pixels in overlapped patches. In addition, the features learned by these methods always contain shifted versions of the same features. In recent years, convolutional sparse coding (CSC) has been developed to address these problems. In this paper, inspired by several successful applications of CSC in the field of signal processing, we explore the potential of CSC in sparse-view CT reconstruction. By directly working on the whole image, without the necessity of dividing the image into overlapped patches in DL-based methods, the proposed methods can maintain more details and avoid artifacts caused by patch aggregation. With predetermined filters, an alternating scheme is developed to optimize the objective function. Extensive experiments with simulated and real CT data were performed to validate the effectiveness of the proposed methods. The qualitative and quantitative results demonstrate that the proposed methods achieve better performance than the several existing state-of-the-art methods.
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