Convolutional Sparse Coding for Compressed Sensing CT Reconstruction

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 addit...

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Vydané v:IEEE transactions on medical imaging Ročník 38; číslo 11; s. 2607 - 2619
Hlavní autori: Bao, Peng, Sun, Huaiqiang, Wang, Zhangyang, Zhang, Yi, Xia, Wenjun, Yang, Kang, Chen, Weiyan, Chen, Mianyi, Xi, Yan, Niu, Shanzhou, Zhou, Jiliu, Zhang, He
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.11.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Coding
Compressed sensing
compressed sensing CT reconstruction
Computed tomography
Computer simulation
Convolutional codes
convolutional sparse coding
Dictionaries
Image coding
Image processing
Image reconstruction
Medical imaging
Objective function
Signal processing
ISSN:0278-0062, 1558-254X, 1558-254X
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2019.2906853