论文|Peng Bao, Wenjun Xia, Kang Yang, Weiyan Chen, Mianyi Chen, Yan Xi, Jiliu Zhou, He Zhang, Huaiqiang Sun, Zhangyang Wang, and Yi Zhang*. Convolutional Sparse Coding for Compressed Sensing CT Reconstruction

本文原载IEEE Transactions on Medical Imaging，由四川大学计算机学院张意副教授等科研人员创作，系四川大学“智慧法治”超前部署学科系列学术成果。后续会持续分享四川大学“智慧法治”超前部署学科系列学术成果，欢迎大家阅读。

Abstract: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.

Index Terms—Computed tomography, compressed sensing CT reconstruction, convolutional sparse coding.

Peng Bao, Wenjun Xia, Kang Yang, Weiyan Chen, Mianyi Chen, Yan Xi, Jiliu Zhou, He Zhang, Huaiqiang Sun, Zhangyang Wang, and Yi Zhang*. Convolutional Sparse Coding for Compressed Sensing CT Reconstruction. IEEE Transactions on Medical Imaging, pp. 2607-2619, vol. 38, no. 11, 2019.（论文下载）