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Abstract #4552

An Unsupervised Method to Enhance Both SNR and Edges for PPI

Weihong Guo1, Feng Huang2

1Department of Mathematics, University of Alabama, Tuscaloosa, AL, USA; 2Advanced Concept Development, Invivo Corporation, Gainesville, FL, USA

Partially parallel imaging (PPI) techniques reduce acquisition time at the cost of signal to noise ratio (SNR). In this work, an unsupervised adaptive method is proposed to reduce noise/artifact level, as well as to sharpen edges. This method is based on Non-local Means (NL-Means). Results of the application to GRAPPA, with both phantom and in vivo data, demonstrate that the proposed method is able to increase SNR, to preserve the fine structures, and to sharpen the edges at the same time.