Cyclic-Consistency for Improved Self-Supervised Learning of Highly Accelerated MRI Reconstruction

Motivation: To improve self-supervised deep learning (DL) reconstruction for highly-accelerated acquisition regimes. Goal(s): To introduce the concept of cyclic-consistency to improve self-supervised DL reconstruction for highly-accelerated MRI. Approach: Cyclic-consistency data is formed by simulating new undersampled acquisitions from the neural network output, with a similar undersampling pattern distribution as the true one. Then reconstruction on these simulated data is trained to match acquired data at the true sampling locations, building cyclic consistency for network training. This is supplemented with a conventional self-supervised masking strategy. Results: The proposed method significantly reduces artifacts at rate 6 and 8 fastMRI reconstruction, and 20-fold fMRI. Impact: Substantial reduction in aliasing artifacts is achieved at high acceleration rates using the proposed cyclic-consistent self-supervised learning method compared to existing self-supervised learning methods.