Improving reference-driven undersampled MRI reconstruction via iterative data correction

When large training datasets are unavailable in real clinical scenarios, researchers turn to unsupervised learning for under-sampled magnetic resonance image reconstruction. However, unsupervised learning methods suffer from insufficient a priori knowledge. We introduce self-consistency constraint with the calibration and acquisition data to tackle these issues. Specifically, we propose an iterative data correction operator to ensure high fidelity of the reconstructed MR data. Experiments shows that the method is flexible and can reconstruct data from arbitrary k-space sampling patterns and easily incorporates additional image priors.