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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Truong Minh-Chinh Tan Tran-Duc Nguyen Linh-Trung Luong, M. Do, M.N. |
| Copyright Year | 2012 |
| Description | Author affiliation: Fac. Electron. & Telecommun., Vietnam Nat. Univ., Hanoi, Vietnam (Truong Minh-Chinh; Tan Tran-Duc; Nguyen Linh-Trung) || Dept. Electr. & Comput. Eng., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA (Do, M.N.) || Lab. Inf. Process. & Transm., Univ. Paris 13, Paris, France (Luong, M.) |
| Abstract | Sweep imaging Fourier transform (SWIFT) is an efficient (fast and quiet) specialized magnetic resonance imaging (MRI) method for imaging tissues or organs that give only short-lived signals due to fast spin-spin relaxation rates. Based on the idea of compressed sensing, this paper proposes a novel method for further enhancing SWIFT using chaotic compressed sensing (CCS-SWIFT). With reduced number of measurements, CCS-SWIFT effectively faster than SWIFT. In comparison with a recently proposed chaotic compressed sensing method for standard MRI (CCS-MRI), simulation results showed that CCS-SWIFT outperforms CCS-MRI in terms of the normalized relative error in the image reconstruction and the probability of exact reconstruction. |
| Sponsorship | IEEE Eng. Medicine Biol. Soc. |
| Starting Page | 380 |
| Ending Page | 383 |
| File Size | 559821 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424441198 |
| ISSN | 1557170X |
| e-ISBN | 9781457717871 |
| DOI | 10.1109/EMBC.2012.6345948 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-08-28 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Magnetic resonance imaging Image reconstruction Compressed sensing Radio frequency Standards Frequency modulation |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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