3-D Total Generalized Variation Method for Dynamic Cardiac MR Image Denoising

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.