A local mutual information guided denoising technique and its application to self-calibrated partially parallel imaging

Abstract

The application of Partially Parallel Imaging (PPI) techniques to regular clinical Magnetic Resonance Imaging (MRI) studies has brought about the benefit of significantly faster acquisitions but at the cost of amplified and spatially variant noise, especially, for high parallel imaging acceleration rates. A Local Mutual Information (LMI) weighted Total Variation (TV) based model is proposed to remove non-evenly distributed noise while preserving image sharpness. For self-calibrated PPI, such as GeneRalized Auto-calibration Partially Parallel Acquisition (GRAPPA) and modified SENSitivity Encoding (mSENSE), a low spatial resolution high Signal to Noise Ratio (SNR) image is available besides the reconstructed high spatial resolution low SNR image. The LMI between these two images is used to detect the noise distribution and the location of edges automatically, and is then applied as guidance for denoising. To better preserve sharpness, Bregman iteration scheme is utilized to add the removed signal back to the denoised image. Entropy of the residual map is used to automatically terminate iteration without using any information of the golden standard or real noise. Results of the proposed algorithm on synthetic and in vivo MR images indicate that the proposed technique preserves image edges and suppresses noise well in the images reconstructed by GRAPPA. The comparison with some existing techniques further confirms the advantages. This algorithm can be applied to enhance the clinical applicability of self-calibrated PPI. Potentially, it can be extended to denoise general images with spatially variant noise.