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A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-thresholding Algorithm with Random Shift
Aim: It can help improve the hospital throughput to accelerate magnetic resonance imaging (MRI) scanning. Patients will benefit from less waiting time. Task: In the last decade, various rapid MRI techniques on the basis of compressed sensing (CS) were proposed. However, both computation time and reconstruction quality of traditional CS-MRI did not meet the requirement of clinical use. Method: In this study, a novel method was proposed with the name of exponential wavelet iterative shrinkage thresholding algorithm with random shift (abbreviated as EWISTARS). It is composed of three successful components: (i) exponential wavelet transform, (ii) iterative shrinkage-thresholding algorithm, and (iii) random shift. Results: Experimental results validated that, compared to state-of-the-art approaches, EWISTARS obtained the least mean absolute error, the leastmean-squared error, and the highest peak signal-to-noise ratio. Conclusion: EWISTARS is superior to state-of-the-art approaches.
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