Directional tensor product complex tight framelets for compressed sensing MRI reconstruction. Issue 12 (13th September 2019)

• Record Type: Journal Article
• Authors: Jiang, Mingfeng; Lu, Liang; Shen, Yi; Wu, Long; Gong, Yinglan; Xia, Ling; Liu, Feng
• Is Part Of: IET image processing. Volume 13:Issue 12(2019)
• Page Start: 2183
• Page End: 2189
• Publication Date: 2019-09-13
• Subjects: wavelet transforms -- tensors -- biomedical MRI -- image reconstruction -- medical image processing -- compressed sensing -- iterative methods -- image denoising -- image representation -- trees (mathematics); directional tensor product complex tight framelets -- compressed sensing MRI reconstruction -- image denoising problem -- soft‐thresholding algorithm -- CS‐MRI reconstruction -- sparse directional representations -- compressed magnetic resonance imaging -- projected fast iterative soft‐thresholding algorithm -- cross‐scale relations -- wavelet tree -- bivariate shrinkage function -- local variance estimation -- shrink thresholding -- image edge preservation -- artefact suppression; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5614
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Compressed sensing magnetic resonance imaging (CS‐MRI) is an effective way of reducing the sampling data in the k ‐space and shortening the scanning time. Motivated by the high performance of directional tensor product complex tight framelets (TPCTFs) for the image denoising problem, the authors proposed a novel framework that integrated TPCTF for sparse representation and projected fast iterative soft‐thresholding algorithm (pFISTA) for CS‐MRI reconstruction. Furthermore, to take advantage of the cross‐scale relations in the wavelet tree of frame coefficients, the bivariate shrinkage (BS) function with local variance estimation is proposed to shrink thresholding. Such TPCTFs can provide sparse directional representations very well for MR image. When compared with other the state‐of‐the‐art CS‐MRI algorithms in numerical experiments, the proposed TPCTF‐BS method achieves a higher reconstruction quality with respect to image edge preservation and the artefact suppression.