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 rec...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2016/9416435 |
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| _version_ | 1832563728947609600 |
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| author | Yudong Zhang Jiquan Yang Jianfei Yang Aijun Liu Ping Sun |
| author_facet | Yudong Zhang Jiquan Yang Jianfei Yang Aijun Liu Ping Sun |
| author_sort | Yudong Zhang |
| collection | DOAJ |
| description | 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 least mean-squared error, and the highest peak signal-to-noise ratio. Conclusion. EWISTARS is superior to state-of-the-art approaches. |
| format | Article |
| id | doaj-art-bc1581a144fe4753b3862bc4726b9647 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-bc1581a144fe4753b3862bc4726b96472025-02-03T01:12:52ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962016-01-01201610.1155/2016/94164359416435A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random ShiftYudong Zhang0Jiquan Yang1Jianfei Yang2Aijun Liu3Ping Sun4Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu 210048, ChinaJiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu 210048, ChinaJiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu 210048, ChinaDepartment of Supply Chain Management, W. P. Carey School of Business, Arizona State University, P.O. Box 873406, Tempe, AZ 85287, USADepartment of Electrical Engineering, The City College of New York, CUNY, New York, NY 10031, USAAim. 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 least mean-squared error, and the highest peak signal-to-noise ratio. Conclusion. EWISTARS is superior to state-of-the-art approaches.http://dx.doi.org/10.1155/2016/9416435 |
| spellingShingle | Yudong Zhang Jiquan Yang Jianfei Yang Aijun Liu Ping Sun A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random Shift International Journal of Biomedical Imaging |
| title | A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random Shift |
| title_full | A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random Shift |
| title_fullStr | A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random Shift |
| title_full_unstemmed | A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random Shift |
| title_short | A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random Shift |
| title_sort | novel compressed sensing method for magnetic resonance imaging exponential wavelet iterative shrinkage thresholding algorithm with random shift |
| url | http://dx.doi.org/10.1155/2016/9416435 |
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