Stationary wavelet transform for under-sampled MRI reconstruction.
Document Type
Article
Publication Date
12-2014
Journal
Magnetic resonance imaging
Volume
32
Issue
10
First Page
1353
Last Page
1364
URL with Digital Object Identifier
10.1016/j.mri.2014.08.004
Abstract
In addition to coil sensitivity data (parallel imaging), sparsity constraints are often used as an additional lp-penalty for under-sampled MRI reconstruction (compressed sensing). Penalizing the traditional decimated wavelet transform (DWT) coefficients, however, results in visual pseudo-Gibbs artifacts, some of which are attributed to the lack of translation invariance of the wavelet basis. We show that these artifacts can be greatly reduced by penalizing the translation-invariant stationary wavelet transform (SWT) coefficients. This holds with various additional reconstruction constraints, including coil sensitivity profiles and total variation. Additionally, SWT reconstructions result in lower error values and faster convergence compared to DWT. These concepts are illustrated with extensive experiments on in vivo MRI data with particular emphasis on multiple-channel acquisitions.
