Keywords: Image Reconstruction, Machine Learning/Artificial IntelligenceDeep learning-based, especially fully supervised learning-based, methods have shown unprecedented performance in MR image reconstruction. Fully sampled data are utilized as references to supervise the learning process. However, it is challenging to acquire fully sampled data in many real-world application scenarios. Unsupervised approaches are required. Here, we propose an iterative data refinement method for enhanced self-supervised MR image reconstruction. Different from Yaman's self-supervised learning method (SSDU), training data in our method are refined iteratively during model optimization to progressively eliminate the data bias between the undersampled reference data and fully sampled data. Better reconstruction results are obtained.
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