Year: 2015
Journal of Fiber Bioengineering and Informatics, Vol. 8 (2015), Iss. 3 : pp. 557–564
Abstract
Total Generalized Variation (TGV) regularization model is one of the most effective methods for MR image denoising. However, for 3D dynamic MR image, the TGV regularization model cannot use the correlated information of each slice. Therefore, in order to effectively denoising the dynamic MR image, 3D Total Generalized total Variation (3D-TGV) is proposed to denoise different kinds noise in the dynamic MR image. Experimental results show that, compared with the Total Variation (TV) and Total Generalized Variation (TGV), the proposed 3D TGV method has a better performance, and can significantly improve the denoising effect, with higher Signal-to-noise Ratio (SNR) and fewer artifacts.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.3993/jfbim00156
Journal of Fiber Bioengineering and Informatics, Vol. 8 (2015), Iss. 3 : pp. 557–564
Published online: 2015-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 8
Keywords: 3D Total Generalized Variation (3D-TGV)
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Joint low-rank prior and difference of Gaussian filter for magnetic resonance image denoising
Chen, Zhen
Zhou, Zhiheng
Adnan, Saifullah
Medical & Biological Engineering & Computing, Vol. 59 (2021), Iss. 3 P.607
https://doi.org/10.1007/s11517-020-02312-8 [Citations: 37]