Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9714
Title: Low-dose X-ray CT reconstruction via dictionary learning
Authors: Xu, Q
Yu, H
Mou, X
Zhang, L 
Hsieh, J
Wang, G
Keywords: Compressive sensing (CS)
Computed tomography (CT)
Dictionary learning
Low-dose CT
Sparse representation
Statistical iterative reconstruction
Issue Date: 2012
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on medical imaging, 2012, v. 31, no. 9, 6188527, p. 1682-1697 How to cite?
Journal: IEEE transactions on medical imaging 
Abstract: Although diagnostic medical imaging provides enormous benefits in the early detection and accuracy diagnosis of various diseases, there are growing concerns on the potential side effect of radiation induced genetic, cancerous and other diseases. How to reduce radiation dose whilemaintaining the diagnostic performance is a major challenge in the computed tomography (CT) field. Inspired by the compressive sensing theory, the sparse constraint in terms of total variation (TV) minimization has already led to promising results for low-doseCT reconstruction.Compared to the discrete gradient transform used in the TV method, dictionary learning is proven to be an effective way for sparse representation. On the other hand, it is important to consider the statistical property of projection data in the low-dose CT case. Recently, we have developed a dictionary learning based approach for lowdose X-ray CT. In this paper, we present this method in detail and evaluate it in experiments. In our method, the sparse constraint in terms of a redundant dictionary is incorporated into an objective function in a statistical iterative reconstruction framework. The dictionary can be either predetermined before an image reconstruction task or adaptively defined during the reconstruction process. An alternating minimization scheme is developed to minimize the objective function. Our approach is evaluated with lowdose X-ray projections collected in animal and human CT studies, and the improvement associated with dictionary learning is quantified relative to filtered backprojection and TV-based reconstructions. The results show that the proposed approach might produce better images with lower noise and more detailed structural features in our selected cases. However, there is no proof that this is true for all kinds of structures.
URI: http://hdl.handle.net/10397/9714
ISSN: 0278-0062 (print)
1558-254X (online)
DOI: 10.1109/TMI.2012.2195669
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