Detail-preserving self-supervised federated learning for undersamped MR image reconstruction

Motivation: Self-supervised learning-based MR image reconstruction learn a prior on the data distribution, but data in medical imaging settings are highly diverse, which consists of different corruptions or degradation factors. Existing methods need improvement in preserving details for undersampled image reconstruction with different degradation factors. Goal(s): Our goal is to preserve details for undersampled image reconstruction with different degradation factors. Approach: A detail-preserving self-supervised federated learning method is proposed to preserve details by employing personalized federated models to refine undersampled training data iteratively. Results: Experiments show that promising results are achieved by proposed method, and details are preserved and refined for undersampled image reconstruction. Impact: Detail-preserving self-supervised federated learning method can effectively preserve more details compared to self-supervised learning methods.