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

• Type: Conference
• Year: 2009
• Where: ICIP
• Keywords: ICIP 2009, Image Processing, Magnetic Resonance Imaging, Regularized Sense Reconstruction, Widely-used Sense Algorithm