@Article{JFBI-8-557,
author = {Mingfeng Jiang, Lulu Han, Yaming Wang, Yu Lu, Nanying Shentu and Guohua Qiu},
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}
}