Exponential wavelet iterative shrinkage thresholding algorithm with random shift for compressed sensing magnetic resonance imaging

Yudong Zhang; Shuihua Wang; Genlin Ji; Zhengchao Dong

doi:10.1002/tee.22059

Exponential wavelet iterative shrinkage thresholding algorithm with random shift for compressed sensing magnetic resonance imaging

Yudong Zhang^*, Shuihua Wang, Genlin Ji, Zhengchao Dong

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

66 Citations (Scopus)

Abstract

We propose the use of exponent of wavelet transform (EWT) coefficients as a sparse representation which is combined with the iterative shrinkage/threshold algorithm (ISTA) for the reconstruction of compressed sensing magnetic resonance imaging. In addition, random shifting (RS) is employed to guarantee the translation invariance property of discrete wavelet transform. The proposed method is termed the exponential wavelet iterative shrinkage/threshold algorithm with random shifting (EWISTARS), which takes advantages of the sparse representation of EWT, the simplicity of ISTA, and the translation invariance of RS. Simulation results on brain, vertebrae, and knee MR images demonstrate that EWISTARS is superior to existing algorithms with regard to reconstruction quality and computation time.

Original language	English
Pages (from-to)	116-117
Number of pages	2
Journal	IEEJ Transactions on Electrical and Electronic Engineering
Volume	10
Issue number	1
DOIs	https://doi.org/10.1002/tee.22059
Publication status	Published - 1 Jan 2015
Externally published	Yes

Keywords

Compressed sensing
Discrete wavelet transform
Magnetic resonance imaging

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10.1002/tee.22059

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title = "Exponential wavelet iterative shrinkage thresholding algorithm with random shift for compressed sensing magnetic resonance imaging",

abstract = "We propose the use of exponent of wavelet transform (EWT) coefficients as a sparse representation which is combined with the iterative shrinkage/threshold algorithm (ISTA) for the reconstruction of compressed sensing magnetic resonance imaging. In addition, random shifting (RS) is employed to guarantee the translation invariance property of discrete wavelet transform. The proposed method is termed the exponential wavelet iterative shrinkage/threshold algorithm with random shifting (EWISTARS), which takes advantages of the sparse representation of EWT, the simplicity of ISTA, and the translation invariance of RS. Simulation results on brain, vertebrae, and knee MR images demonstrate that EWISTARS is superior to existing algorithms with regard to reconstruction quality and computation time.",

keywords = "Compressed sensing, Discrete wavelet transform, Magnetic resonance imaging",

author = "Yudong Zhang and Shuihua Wang and Genlin Ji and Zhengchao Dong",

note = "Publisher Copyright: {\textcopyright} 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.",

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AU - Zhang, Yudong

AU - Wang, Shuihua

AU - Ji, Genlin

AU - Dong, Zhengchao

PY - 2015/1/1

Y1 - 2015/1/1

N2 - We propose the use of exponent of wavelet transform (EWT) coefficients as a sparse representation which is combined with the iterative shrinkage/threshold algorithm (ISTA) for the reconstruction of compressed sensing magnetic resonance imaging. In addition, random shifting (RS) is employed to guarantee the translation invariance property of discrete wavelet transform. The proposed method is termed the exponential wavelet iterative shrinkage/threshold algorithm with random shifting (EWISTARS), which takes advantages of the sparse representation of EWT, the simplicity of ISTA, and the translation invariance of RS. Simulation results on brain, vertebrae, and knee MR images demonstrate that EWISTARS is superior to existing algorithms with regard to reconstruction quality and computation time.

AB - We propose the use of exponent of wavelet transform (EWT) coefficients as a sparse representation which is combined with the iterative shrinkage/threshold algorithm (ISTA) for the reconstruction of compressed sensing magnetic resonance imaging. In addition, random shifting (RS) is employed to guarantee the translation invariance property of discrete wavelet transform. The proposed method is termed the exponential wavelet iterative shrinkage/threshold algorithm with random shifting (EWISTARS), which takes advantages of the sparse representation of EWT, the simplicity of ISTA, and the translation invariance of RS. Simulation results on brain, vertebrae, and knee MR images demonstrate that EWISTARS is superior to existing algorithms with regard to reconstruction quality and computation time.

KW - Compressed sensing

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Exponential wavelet iterative shrinkage thresholding algorithm with random shift for compressed sensing magnetic resonance imaging

Abstract

Keywords

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