Denoising intrinsic MRI repetitions using self-supervised iterative residual learning

Motivation: MRI with high resolution and/or acceleration factor suffers from intrinsic low signal-to-noise ratio. Supervised learning-based denoising significantly improves image quality, but requires high-SNR data as training targets. Goal(s): To denoise images using noisy image repetitions without additional acquisition. Approach: Noise2Average trains CNN to map each noisy image to its residual compared to the average of all noisy images at iteration 1 and all denoised images from iteration k-1 at iteration k. The images from opposite phase-encoding directions of EPI or different echo times of ME-MPRAGE are noisy repetitions. Results: Noise2Average outperforms BM4D, AONLM and Noise2Noise in terms of image quality and DTI metrics. Impact: By reducing the requirement for training data and time, Noise2Average substantially increases the feasibility and accessibility of deep learning based denoising methods for MRI and potentially benefits a wider range of clinical and neuroscientific studies.