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Abstract #0773

Progressively Distribution-based Rician Noise Removal for Magnetic Resonance Imaging

Qiegen Liu1, Sanqian Li1, Jiujie Lv1, and Dong Liang2

1Department of Electronic Information Engineering, Nanchang University, nanchang, China, 2Lauterbur Research Centre for Biomedical Imaging, Shenzhen Key Laboratory for MRI, Shenzhen Institutes of Advanced Technology, Shenzhen, China

Different from the existing MRI denoising methods that utilizing the spatial neighbor information around the pixels or patches, this work turns to capture the pixel-level distribution information by means of supervised network learning. A wide and progressive network learning strategy is proposed, via fitting the distribution at pixel-level and feature-level with large convolutional filters. The whole network is trained in a two-stage fashion, consisting of the residual network in pixel domain with batch normalization layer and in feature domain without batch normalization layer. Experiments demonstrate its great potential with substantially improved SNR and preserved edges and structures.

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