Volume 8, Issue 3
3-D Total Generalized Variation Method for Dynamic Cardiac MR Image Denoising

Mingfeng Jiang, Lulu Han, Yaming Wang, Yu Lu, Nanying Shentu & Guohua Qiu

Journal of Fiber Bioengineering & Informatics, 8 (2015), pp. 557-564.

Published online: 2015-08

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  • 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.

  • Keywords

3D Total Generalized Variation (3D-TGV) Dynamic MR Imaging Denoising Total Variation (TV)

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COPYRIGHT: © Global Science Press

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@Article{JFBI-8-557, author = {}, title = {3-D Total Generalized Variation Method for Dynamic Cardiac MR Image Denoising}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2015}, volume = {8}, number = {3}, pages = {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.}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbim00156}, url = {http://global-sci.org/intro/article_detail/jfbi/4737.html} }
TY - JOUR T1 - 3-D Total Generalized Variation Method for Dynamic Cardiac MR Image Denoising JO - Journal of Fiber Bioengineering and Informatics VL - 3 SP - 557 EP - 564 PY - 2015 DA - 2015/08 SN - 8 DO - http://doi.org/10.3993/jfbim00156 UR - https://global-sci.org/intro/article_detail/jfbi/4737.html KW - 3D Total Generalized Variation (3D-TGV) KW - Dynamic MR Imaging KW - Denoising KW - Total Variation (TV) AB - 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.
Mingfeng Jiang, Lulu Han, Yaming Wang, Yu Lu, Nanying Shentu & Guohua Qiu. (2019). 3-D Total Generalized Variation Method for Dynamic Cardiac MR Image Denoising. Journal of Fiber Bioengineering and Informatics. 8 (3). 557-564. doi:10.3993/jfbim00156
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