Simultaneous self-supervised reconstruction and denoising for low SNR, sub-sampled training data with Robust SSDU

Motivation: For low SNR training data, such as from low-field scanners, sub-sampled images reconstructed via deep learning can be susceptible to errors due to measurement noise. Goal(s): To evaluate the performance of the proposed Robust Self-Supervised Learning via Data Undersampling (Robust SSDU), which removes corruptions due to aliasing and measurement noise in an entirely self-supervised manner. Approach: On the fastMRI dataset and low-field dataset M4Raw, Robust SSDU was compared with a number of benchmarks including supervised training. Results: Robust SSDU exhibited a substantially higher fidelity image restoration than standard SSDU and sharper reconstructions than competing methods that remove measurement noise. Impact: This study demonstrates that high quality image reconstruction with deep learning is achievable when only sub-sampled, low SNR data is available for training. The proposed method could particularly impact the diagnostic potential of images acquired from low field scanners.