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Abstract #1243

Adaptive Multi-contrast MR Image Denoising based on a Residual U-Net using Noise Level Map

Jiahao Hu1,2,3, Yilong Liu1,2, Zheyuan Yi1,2,3, Yujiao Zhao1,2, Fei Chen3, and Ed X. Wu1,2
1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China, 2Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China, 3Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China

Multi-contrast MRI offers us images with complementary diagnostic information. Despite the dramatic difference in contrast, multi-contrast images often share highly correlated structure information. A deep learning (DL) based strategy is proposed to denoise multi-contrast MR images with flexible noise-levels using residual U-Net. This method utilizes the structural similarities across contrasts by simultaneously denoising multiple contrasts while existing single-contrast MRI denoising methods neglect the analogous structure information. The proposed method outperforms BM3D in terms of better noise reduction and details preservation. More importantly, we introduce a noise-level map that can be manually set to fit the different noise levels.

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