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

机译:压缩感知磁共振成像的指数小波迭代收缩阈值算法

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摘要

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.
机译:加速MRI扫描对医院和患者都是有益的。最近,提出了一种基于CS的新的快速MRI技术。然而,CS-MRI的重建质量和计算时间不符合临床使用标准。因此,我们提出了一种基于三个成功组件的新颖算法:EWT的稀疏性,FISTA的快速性和SISTA中的出色调整。该方法被称为指数小波迭代收缩/阈值算法(EWISTA)。对四种MR图像(大脑,踝关节,膝盖和ADHD)进行的实验表明,所提出的EWISTA的重建性能要优于诸如FCSA,ISTA,FISTA,SISTA和EWT-ISTA等最新算法。 。此外,EWISTA比ISTA和EVVT-ISTA快,但比FCSA,FISTA和SISTA稍慢。 (C)2015 Elsevier Inc.保留所有权利。

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