Compressive sensing MR imaging based on adaptive tight frame and reference image. Issue 14 (5th November 2020)

• Record Type: Journal Article
• Authors: Cao, Chunhong; Duan, Wei; Hu, Kai; Xiao, Fen
• Is Part Of: IET image processing. Volume 14:Issue 14(2020)
• Page Start: 3508
• Page End: 3515
• Publication Date: 2020-11-05
• Subjects: image reconstruction -- compressed sensing -- iterative methods -- magnetic resonance imaging; adaptive tight frame -- compressive sensing magnetic resonance imaging -- MR image reconstruction -- compressive sensing theory -- named reference image -- MR image reconstruction method; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0834
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Compressive sensing magnetic resonance (MR) imaging is aimed at achieving high‐quality MR image reconstruction by undersampling K‐space data. It is crucial to explore prior information since compressive sensing MR imaging relies heavily on some prior assumptions, such as signal's sparse property. In this study, in order to explore the prior information fully, an improved MR image reconstruction model based on compressive sensing theory is proposed, named reference image MR imaging with adaptive tight frame. In the proposed model, an adaptive tight frame is involved to explore the sparse prior information adapt to MR images and the similarity prior information to the target image. Meanwhile, improved adaptive weighting parameters are used to trade off the sparsity between the regions with much similarity and that of little similarity. In addition, the smoothing‐based fast iterative shrinkage‐threshold algorithm is utilised to tackle the optimisation problem so as to speed up imaging. The experimental results demonstrate that the proposed MR image reconstruction method outperforms some state‐of‐the‐art methods in terms of quantitative results.