NDLI: Variational Regularized Tree-Structured Wavelet Sparsity for CS-SENSE Parallel Imaging

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Ryan Wen Liu Quandang Ma Simon Chun Ho Yu Kwok Tai Chui Naixue Xiong
Abstract	Both compressed sensing magnetic resonance imaging (MRI) and parallel MRI have emerged as effective techniques to accelerate MRI data acquisition in various clinical applications. The hybrid parallel imaging reconstruction methods by combining these two techniques have been developed for providing further acceleration. However, the widely used $L_{1}$-norm of wavelet coefficients and total variation (TV) regularizer in traditional hybrid imaging methods limited further improvement in image quality. To further enhance imaging quality and reduce acquisition time, we proposed a regularized parallel imaging reconstruction method by incorporating sparsity-promoting wavelet prior and total generalized variation (TGV) regularizer. Specifically, the wavelet sparsity is effectively promoted through the $L_{0}$ quasinorm of wavelet coefficients and tree-structured wavelet representation. This sparsity-promoting wavelet prior is capable of representing a better measure of sparseness to guarantee high-quality reconstruction even for high degrees of undersampling. Unlike TV regularizer, which preserves sharp edges but suffers from staircaselike artifacts, TGV regularizer can balance the tradeoff between edges preservation and artifacts suppression. Numerous experiments have been conducted on both simulated and in vivo MRI data sets to compare our proposed method with some state-of-the-art reconstruction methods. Experimental results have demonstrated its superior imaging performance in terms of both quantitative evaluation and visual quality.
e-ISSN	21693536
DOI	10.1109/ACCESS.2018.2874382
Journal	IEEE Access
Volume Number	6
Language	English
Publisher	IEEE
Publisher Date	2018-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Electrical Engineering. Electronics. Nuclear Engineering Compressed Sensing (cs) Magnetic Resonance Imaging (mri) Sense Total Generalized Variation (tgv) Tree-structured Sparsity
Content Type	Text
Resource Type	Article

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