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Abstract #4338

Noise2Average: deep learning based denoising without high-SNR training data using iterative residual learning

Zihan Li1, Berkin Bilgic2,3, Ziyu Li4, Kui Ying5, Jonathan R. Polimeni2,3, Susie Huang2,3, and Qiyuan Tian2,3
1Department of Biomedical Engineering, Tsinghua University, Beijing, China, 2Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States, 3Harvard Medical School, Boston, MA, United States, 4Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom, 5Department of Engineering Physics, Tsinghua University, Beijing, China


The requirement for high-SNR reference data reduces the feasibility of supervised deep learning-based denoising. Noise2Noise addresses this challenge by learning to map one noisy image to another repetition of the noisy image but suffers from image blurring resulting from imperfect image alignment and intensity mismatch for empirical MRI data. A novel approach, Noise2Average, is proposed to improve Noise2Noise, which employs supervised residual learning to preserve image sharpness and transfer learning for subject-specific training. Noise2Average is demonstrated effective in denoising empirical Wave-CAIPI MPRAGE T1-weighted data (R=3×3-fold accelerated) and DTI data and outperforms Noise2Noise and state-of-the-art BM4D and AONLM denoising methods.

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