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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kashyap, S. Jacob, M. |
| Copyright Year | 2010 |
| Description | Author affiliation: Department of Electrical and Computer Engineering, University of Rochester, NY, USA (Kashyap, S.) || Department of Biomedical Engineering, University of Rochester, NY, USA (Jacob, M.) |
| Abstract | We introduce a novel algorithm for regularized reconstruction of non-Cartesian MRI data. The proposed non-iterative scheme closely approximates the Tikhonov regularized least squares method, but provides a significant speed up over standard implementation based on iterative conjugate gradient algorithm. This computational complexity of the proposed scheme is comparable to that of gridding. However, the proposed scheme is significantly more robust to undersampling and measurement noise. Numerical simulations clearly demonstrate the advantages of the proposed algorithm over traditional schemes. The proposed algorithm may be very useful in dynamic and functional MRI applications, where the fast reconstruction of several undersampled images is required. |
| Starting Page | 265 |
| Ending Page | 268 |
| File Size | 559812 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424441259 |
| ISSN | 19457928 |
| DOI | 10.1109/ISBI.2010.5490362 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-04-14 |
| Publisher Place | Netherlands |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Magnetic resonance imaging Iterative algorithms Image reconstruction Least squares approximation Least squares methods Iterative methods Computational complexity Noise robustness Noise measurement Numerical simulation Thikanov Regularization Magnetic Resonance Imaging Sparse Reconstruction Orthogonal Matching Pursuits |
| Content Type | Text |
| Resource Type | Article |
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