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

Yudong Zhang*, Zhengchao Dong, Preetha Phillips, Shuihua Wang, Genlin Ji, Jiquan Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

103 Citations (Scopus)

Abstract

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 EWT-ISTA, but slightly slower than FCSA, FISTA and SISTA.

Original languageEnglish
Article number11611
Pages (from-to)115-132
Number of pages18
JournalInformation Sciences
Volume322
DOIs
Publication statusPublished - 20 Nov 2015
Externally publishedYes

Keywords

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

Fingerprint

Dive into the research topics of 'Exponential Wavelet Iterative Shrinkage Thresholding Algorithm for compressed sensing magnetic resonance imaging'. Together they form a unique fingerprint.

Cite this