Noise2Recon: A Semi-Supervised Framework for Joint MRI Reconstruction and Denoising

Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, standard supervised DL methods depend on extensive amounts of fully-sampled ground-truth data and are sensitive to out-of-distribution (OOD) shifts, in particular for low signal-to-noise ratio (SNR) acquisitions. To alleviate this challenge, we propose a semi-supervised, consistency-based framework (termed Noise2Recon) for joint MR reconstruction and denoising. Our method enables the usage of a limited number of fully-sampled and a large number of undersampled-only scans. We compare our method to augmentation-based supervised techniques and fine-tuned denoisers. Results demonstrate that even with minimal ground-truth data, Noise2Recon (1) achieves high performance on in-distribution (low-noise) scans and (2) improves generalizability to OOD, noisy scans.