Title

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.

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