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Abstract #0669

Simultaneous Denoising of Multi-contrast MR Images Using a Novel Weighted Nuclear Norm Minimization Approach

Yujiao Zhao1,2, Yilong Liu1,2, Henry Ka-Fung Mak3, and Ed X. Wu1,2

1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China, 2Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China, 3Diagnostic Radiology, The University of Hong Kong, Hong Kong, China

A typical clinical MRI scanning session produces image sets with same geometries but different contrasts. These multi-contrast images often share strong structural similarities or correlations despite their contrast differences. Most existing MRI denoising methods deal with single-contrast images independently, and fail to explore and utilize such correlations across contrasts. In this study, we present a simultaneous denoising method for multi-contrast images based on low rank multi-contrast patch matrix completion. This denoising method exploits the structural similarities across contrasts, and outperforms the traditional method. Further, it does not compromise the image fidelity in absence of any structural similarities across contrasts.

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