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2009
IEEE

Wavelet-based Parallel Mri Regularization Using Bivariate Sparsity Promoting Priors

10 years 5 months ago
Wavelet-based Parallel Mri Regularization Using Bivariate Sparsity Promoting Priors
Parallel magnetic resonance imaging (pMRI) using multiple receiver coils has emerged as a powerful 3D imaging technique for reducing scanning time or increasing image resolution. The acquired k-space is subsampled, and full Field of View (FoV) images are then reconstructed from the acquired aliased data, by applying methods such as the widely-used SENSE algorithm. However, reconstructed images using SENSE may suffer from several kinds of artifacts mainly because of the noise and inaccurate sensitivity profiles. In this paper, we propose an appproach for regularized SENSE reconstruction in the wavelet transform domain. More precisely, a Bayesian strategy is adopted by introducing a bivariate prior to model the complex-valued signal. Experiments on synthetic data and real T1
Added 10 Nov 2009
Updated 26 Dec 2009
Type Conference
Year 2009
Where ICIP
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