  Deep learning (DL) has shown promise for faster, high quality accelerated MRI
reconstruction. However, supervised DL methods depend on extensive amounts of
fully-sampled (labeled) data and are sensitive to out-of-distribution (OOD)
shifts, particularly low signal-to-noise ratio (SNR) acquisitions. To alleviate
this challenge, we propose Noise2Recon, a model-agnostic, consistency training
method for joint MRI reconstruction and denoising that can use both
fully-sampled (labeled) and undersampled (unlabeled) scans in semi-supervised
and self-supervised settings. With limited or no labeled training data,
Noise2Recon outperforms compressed sensing and deep learning baselines,
including supervised networks, augmentation-based training, fine-tuned
denoisers, and self-supervised methods, and matches performance of supervised
models, which were trained with 14x more fully-sampled scans. Noise2Recon also
outperforms all baselines, including state-of-the-art fine-tuning and
augmentation techniques, among low-SNR scans and when generalizing to other OOD
factors, such as changes in acceleration factors and different datasets.
Augmentation extent and loss weighting hyperparameters had negligible impact on
Noise2Recon compared to supervised methods, which may indicate increased
training stability. Our code is available at https://github.com/ad12/meddlr.
