Wavelet-regularized reconstruction for rapid MRI

Authors:
M. Guerquin-Kern;D. Van De Ville;C. Vonesch;J.-C. Baritaux;K. P. Pruessmann;M. Unser
Affiliations:
Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne, Lausanne;Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne, Lausanne;Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne, Lausanne;Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne, Lausanne;Institute for Biomedical Engineering, University and ETH Zurich, Zürich;Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne, Lausanne
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

Citing 3
Cited 1

A fast multilevel algorithm for wavelet-regularized image restoration

IEEE Transactions on Image Processing
Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage

IEEE Transactions on Image Processing
An EM algorithm for wavelet-based image restoration

IEEE Transactions on Image Processing

On the choice of Compressed Sensing priors and sparsifying transforms for MR image reconstruction: An experimental study

Image Communication

Quantified Score

Hi-index	0.00

Visualization

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.