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...

• Type: Conference
• Year: 2008
• Where: MICCAI
• Authors: Weihong Guo, Feng Huang
• Keywords: Medical Imaging, MICCAI 2008, Noise Distribution, Real Noise, Variant Noise