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A Model of Regularization Parameter Determination in Low-Dose X-Ray CT Reconstruction Based on Dictionary Learning
Zhang, C.; T. Zhang; J. Zheng; M. Li; Y. F. Lu; J. L. You and Y. H. Guan
2015
发表期刊Computational and Mathematical Methods in Medicine
页码12
摘要In recent years, X-ray computed tomography (CT) is becoming widely used to reveal patient's anatomical information. However, the side effect of radiation, relating to genetic or cancerous diseases, has caused great public concern. The problem is how to minimize radiation dose significantly while maintaining image quality. As a practical application of compressed sensing theory, one category of methods takes total variation (TV) minimization as the sparse constraint, which makes it possible and effective to get a reconstruction image of high quality in the undersampling situation. On the other hand, a preliminary attempt of low-dose CT reconstruction based on dictionary learning seems to be another effective choice. But some critical parameters, such as the regularization parameter, cannot be determined by detecting datasets. In this paper, we propose a reweighted objective function that contributes to a numerical calculation model of the regularization parameter. A number of experiments demonstrate that this strategy performs well with better reconstruction images and saving of a large amount of time.
收录类别SCI
文献类型期刊论文
条目标识符http://ir.ciomp.ac.cn/handle/181722/55513
专题中科院长春光机所知识产出
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GB/T 7714
Zhang, C.,T. Zhang,J. Zheng,et al. A Model of Regularization Parameter Determination in Low-Dose X-Ray CT Reconstruction Based on Dictionary Learning[J]. Computational and Mathematical Methods in Medicine,2015:12.
APA Zhang, C.,T. Zhang,J. Zheng,M. Li,Y. F. Lu,&J. L. You and Y. H. Guan.(2015).A Model of Regularization Parameter Determination in Low-Dose X-Ray CT Reconstruction Based on Dictionary Learning.Computational and Mathematical Methods in Medicine,12.
MLA Zhang, C.,et al."A Model of Regularization Parameter Determination in Low-Dose X-Ray CT Reconstruction Based on Dictionary Learning".Computational and Mathematical Methods in Medicine (2015):12.
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