Fast Compressed Sensing MRI Based on Complex Double-Density Dual-Tree Discrete Wavelet Transform
Compressed sensing (CS) has been applied to accelerate magnetic resonance imaging (MRI) for many years. Due to the lack of translation invariance of the wavelet basis, undersampled MRI reconstruction based on discrete wavelet transform may result in serious artifacts. In this paper, we propose a CS-...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
|
| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2017/9604178 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832547568904568832 |
|---|---|
| author | Shanshan Chen Bensheng Qiu Feng Zhao Chao Li Hongwei Du |
| author_facet | Shanshan Chen Bensheng Qiu Feng Zhao Chao Li Hongwei Du |
| author_sort | Shanshan Chen |
| collection | DOAJ |
| description | Compressed sensing (CS) has been applied to accelerate magnetic resonance imaging (MRI) for many years. Due to the lack of translation invariance of the wavelet basis, undersampled MRI reconstruction based on discrete wavelet transform may result in serious artifacts. In this paper, we propose a CS-based reconstruction scheme, which combines complex double-density dual-tree discrete wavelet transform (CDDDT-DWT) with fast iterative shrinkage/soft thresholding algorithm (FISTA) to efficiently reduce such visual artifacts. The CDDDT-DWT has the characteristics of shift invariance, high degree, and a good directional selectivity. In addition, FISTA has an excellent convergence rate, and the design of FISTA is simple. Compared with conventional CS-based reconstruction methods, the experimental results demonstrate that this novel approach achieves higher peak signal-to-noise ratio (PSNR), larger signal-to-noise ratio (SNR), better structural similarity index (SSIM), and lower relative error. |
| format | Article |
| id | doaj-art-521d065961c44650a2586bb0d3b3c567 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-521d065961c44650a2586bb0d3b3c5672025-02-03T06:44:20ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962017-01-01201710.1155/2017/96041789604178Fast Compressed Sensing MRI Based on Complex Double-Density Dual-Tree Discrete Wavelet TransformShanshan Chen0Bensheng Qiu1Feng Zhao2Chao Li3Hongwei Du4Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027, ChinaCenters for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027, ChinaSchool of Computer Science, University of Lincoln, Brayford Pool, Lincoln LN6 7TS, UKCenters for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027, ChinaCenters for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027, ChinaCompressed sensing (CS) has been applied to accelerate magnetic resonance imaging (MRI) for many years. Due to the lack of translation invariance of the wavelet basis, undersampled MRI reconstruction based on discrete wavelet transform may result in serious artifacts. In this paper, we propose a CS-based reconstruction scheme, which combines complex double-density dual-tree discrete wavelet transform (CDDDT-DWT) with fast iterative shrinkage/soft thresholding algorithm (FISTA) to efficiently reduce such visual artifacts. The CDDDT-DWT has the characteristics of shift invariance, high degree, and a good directional selectivity. In addition, FISTA has an excellent convergence rate, and the design of FISTA is simple. Compared with conventional CS-based reconstruction methods, the experimental results demonstrate that this novel approach achieves higher peak signal-to-noise ratio (PSNR), larger signal-to-noise ratio (SNR), better structural similarity index (SSIM), and lower relative error.http://dx.doi.org/10.1155/2017/9604178 |
| spellingShingle | Shanshan Chen Bensheng Qiu Feng Zhao Chao Li Hongwei Du Fast Compressed Sensing MRI Based on Complex Double-Density Dual-Tree Discrete Wavelet Transform International Journal of Biomedical Imaging |
| title | Fast Compressed Sensing MRI Based on Complex Double-Density Dual-Tree Discrete Wavelet Transform |
| title_full | Fast Compressed Sensing MRI Based on Complex Double-Density Dual-Tree Discrete Wavelet Transform |
| title_fullStr | Fast Compressed Sensing MRI Based on Complex Double-Density Dual-Tree Discrete Wavelet Transform |
| title_full_unstemmed | Fast Compressed Sensing MRI Based on Complex Double-Density Dual-Tree Discrete Wavelet Transform |
| title_short | Fast Compressed Sensing MRI Based on Complex Double-Density Dual-Tree Discrete Wavelet Transform |
| title_sort | fast compressed sensing mri based on complex double density dual tree discrete wavelet transform |
| url | http://dx.doi.org/10.1155/2017/9604178 |
| work_keys_str_mv | AT shanshanchen fastcompressedsensingmribasedoncomplexdoubledensitydualtreediscretewavelettransform AT benshengqiu fastcompressedsensingmribasedoncomplexdoubledensitydualtreediscretewavelettransform AT fengzhao fastcompressedsensingmribasedoncomplexdoubledensitydualtreediscretewavelettransform AT chaoli fastcompressedsensingmribasedoncomplexdoubledensitydualtreediscretewavelettransform AT hongweidu fastcompressedsensingmribasedoncomplexdoubledensitydualtreediscretewavelettransform |