Name
Abstract | ||
|---|---|---|
We propose a reconstruction scheme adapted to MRI that takes advantage of a sparsity constraint in the wavelet domain. We show that artifacts are significantly reduced compared to conventional reconstruction methods. Our approach is also competitive with Total Variation regularization both in terms of MSE and computation time. We show that l1 regularization allows partial recovery of the missing k-space regions. We also present a multi-level version that significantly reduces the computational cost. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ISBI.2009.5193016 | ISBI |
Keywords | Field | DocType |
rapid mri,missing k-space region,sparsity constraint,computation time,multi-level version,conventional reconstruction method,partial recovery,computational cost,reconstruction scheme,l1 regularization,wavelet-regularized reconstruction,total variation regularization,noise,sparsity,algorithm,compressed sensing,mse,wavelets,image reconstruction,multiresolution analysis,tv,mri,data mining,trajectory,reconstruction,wavelet transforms,image restoration,indexing terms,magnetic resonance imaging | Iterative reconstruction,Computer vision,Pattern recognition,Computer science,Multiresolution analysis,Total variation denoising,Regularization (mathematics),Artificial intelligence,Image restoration,Compressed sensing,Wavelet,Wavelet transform | Conference |
Citations | PageRank | References |
8 | 0.72 | 4 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Guerquin-Kern | 1 | 10 | 1.16 |
| Dimitri Van De Ville | 2 | 1656 | 118.48 |
| C. Vonesch | 3 | 224 | 17.13 |
| J.-C. Baritaux | 4 | 19 | 2.61 |
| K. P. Pruessmann | 5 | 10 | 1.16 |
| Unser, M. | 6 | 3438 | 442.40 |