TITLE:
A wavelet-based super-resolution method for multi-slice MRI
AUTHORS:
Rafiqul Islam, Andrew J. Lambert, Mark R. Pickering, Jennie M. Scarvell, Paul N. Smith
KEYWORDS:
Magnetic Resonance Imaging; Super Resolution; Gaussian Scale Mixture Model; Wavelet Regularization
JOURNAL NAME:
Journal of Biomedical Science and Engineering,
Vol.5 No.12A,
December
31,
2012
ABSTRACT:
In multi-slice magnetic resonance imaging (MRI), the
resolution in the slice direction is usually reduced to allow faster
acquisition times and to reduce the amount of noise in each 2D slice. To
address this issue, a number of super resolution (SR) methods have been
proposed to improve the resolution of 3D MRI volumes. Most of the methods
involve the use of prior models of the MRI data as regularization terms in an
ill-conditioned inverse problem. The use of user-defined parameters produces
better results for these approaches but an inappropriate choice may reduce the
overall performance of the algorithm. In this paper, we present a wavelet
domain SR method which uses a Gaussian scale mixture (GSM) model in a
sparseness constraint to regularize the ill-posed SR inverse problem. The
proposed approach also makes use of an extension of the Dual Tree Complex Wavelet Transform to provide the ability to analyze the wavelet coefficients with
sub-level precision. Our results show that the 3D MRI volumes reconstructed
using this approach have quality superior to volumes produced by the best
previously proposed approaches.