Name
Abstract | ||
|---|---|---|
Compressive sensing (CS) MRI aims to accurately reconstruct images from undersampled k-space data. Most CS methods employ analytical sparsifying transforms such as total-variation and wavelets to model the unknown image and constrain the solution space during reconstruction. Recently, nonparametric dictionary-based methods for CS-MRI reconstruction have shown significant improvements over the classical methods. These existing techniques focus on learning the representation basis for the unknown image for a synthesis-based reconstruction. In this paper, we present a new framework for analysis-based reconstruction, where the sparsifying transform is learnt from a reference image to capture the anatomical structure of unknown image, and is used to guide the reconstruction process. We demonstrate with experimental data the high performance of the proposed approach over traditional methods. © 2011 IEEE. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/IEMBS.2011.6091384 | Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference |
Keywords | Field | DocType |
total variation,compressed sensing,image reconstruction,magnetic resonance imaging,sparse matrices,data compression,dictionaries,algorithms,magnetic resonance image | Iterative reconstruction,Computer vision,Experimental data,Pattern recognition,Computer science,Reference image,Nonparametric statistics,Artificial intelligence,Data compression,Sparse matrix,Compressed sensing,Wavelet | Conference |
Volume | Issue | ISSN |
null | null | null |
ISBN | Citations | PageRank |
978-1-4244-4122-8 | 4 | 1.07 |
References | Authors | |
2 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sevket Derin Babacan | 1 | 4 | 1.07 |
| Xi Peng | 2 | 4 | 1.07 |
| Xian-Pei Wang | 3 | 4 | 2.42 |
| Minh N. Do | 4 | 1681 | 133.55 |
| Zhi-Pei Liang | 5 | 522 | 64.94 |