Alternating total variation and non‐local total variation for fast compressed sensing magnetic resonance imaging. Issue 22 (14th October 2015)

• Record Type: Journal Article
• Authors: Hao, Wangli; Li, Jianwu
• Is Part Of: Electronics letters. Volume 51:Issue 22(2015)
• Page Start: 1740
• Page End: 1742
• Publication Date: 2015-10-14
• Subjects: biomedical MRI -- compressed sensing -- medical image processing -- image sampling -- image restoration -- wavelet transforms -- optimisation -- iterative methods -- computational complexity; alternating total variation -- nonlocal total variation -- fast compressed sensing magnetic resonance imaging -- total variance‐based compressed sensing MRI reconstruction methods -- restoring magnetic resonance image structure -- undersampled k‐space data -- block effects -- image edges -- wavelet regularisations -- optimisation method -- wavelet constraints -- iteration -- reconstruction accuracy -- computation complexity; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2015.2551
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : Total variance‐(TV) based compressed sensing MRI (CS‐MRI) reconstruction methods are effective in restoring a magnetic resonance (MR) image structure from undersampled k ‐space data. However, the local details of MR images are usually oversmoothed such that block effects are easily caused. Fortunately, the problem can be overcome by the non‐local total variation (NLTV), which is highly effective in keeping fine details and sharping image edges. Nevertheless, NLTV is not good at finding similar patches. Considering TV and NLTV are complementary, it is proposed to use them alternatively instead of combining them in one objective function for CS‐MRI. In one alternation, the objective function containing the TV and the wavelet regularisations is firstly used to build the structure of the reconstructed MR image through running several iterations of the optimisation method and then the objective function including the NLTV and the wavelet constraints is used to remove the blocking effects and to preserve the image details through running only one iteration of the optimisation method. A number of alternations are run to find the final reconstruction image. Experimental results validate the effectiveness of the method regarding both reconstruction accuracy and computation complexity.