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

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 th...

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Veröffentlicht in:Information sciences Jg. 322; S. 115 - 132
Hauptverfasser: Zhang, Yudong, Dong, Zhengchao, Phillips, Preetha, Wang, Shuihua, Ji, Genlin, Yang, Jiquan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 20.11.2015
Schlagworte:
Algorithms
Compressed sensing
Iterative methods
Iterative Shrinkage/Thresholding Algorithm
Magnetic resonance imaging
Patients
Reconstruction
Shrinkage
Sparsifying transform
Sparsity measure
Tuning
Wavelet
Iterative Shrinkage/Thresholding Algorithm
Magnetic resonance imaging
Sparsifying transform
Compressed sensing
Sparsity measure
ISSN:0020-0255
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0020-0255
DOI:10.1016/j.ins.2015.06.017