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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