4.7 Article

Exponential Wavelet Iterative Shrinkage Thresholding Algorithm for compressed sensing magnetic resonance imaging

期刊

INFORMATION SCIENCES
卷 322, 期 -, 页码 115-132

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2015.06.017

关键词

Magnetic resonance imaging; Compressed sensing; Sparsity measure; Sparsifying transform; Iterative Shrinkage/Thresholding Algorithm

资金

  1. NSFC [610011024, 61273243, 51407095]
  2. Program of Natural Science Research of Jiangsu Higher Education Institutions [13KJB460011, 14KJB520021]
  3. Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing [BM2013006]
  4. Key Supporting Science and Technology Program (Industry) of Jiangsu Province [BE2012201, BE2014009-3, BE2013012-2]
  5. Special Funds for Scientific and Technological Achievement Transformation Project in Jiangsu Province [BA2013058]
  6. Nanjing Normal University Research Foundation [2013119XGQ0061, 2014119XGQ0080]

向作者/读者索取更多资源

It is beneficial for both hospitals and patients to accelerate MRI scanning. Recently, a new fast MRI technique based on CS was proposed. However, the reconstruction quality and computation time of CS-MRI did not meet the standard of clinical use. Therefore, we proposed a novel algorithm based on three successful components: the sparsity of EWT, the rapidness of FISTA, and the excellent tuning in SISTA. The proposed method was dubbed Exponential Wavelet Iterative Shrinkage/Threshold Algorithm (EWISTA). Experiments over four kinds of MR images (brain, ankle, knee, and ADHD) indicated that the proposed EWISTA showed better reconstruction performance than the state-of-the-art algorithms such as FCSA, ISTA, FISTA, SISTA, and EWT-ISTA. Moreover, EWISTA was faster than ISTA and EVVT-ISTA, but slightly slower than FCSA, FISTA and SISTA. (C) 2015 Elsevier Inc. All rights reserved.

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