Generalizable and Accurate Federated learning for Fast MR imaging Equipped with Laplacian Attention Mechanism

Motivation: Federated MR image reconstruction can make full use of data from multiple institutions while protecting patient privacy, but the images obtained by existing methods still need improvement in terms of fine structures. Goal(s): Our goal is to improve the quality of clinical diagnosis by achieving accurate MR image reconstruction. Approach: A Laplacian attention mechanism is proposed to capture fine structure and details for accurate MR image reconstruction from undersampled data. Results: Qualitative and quantitative experimental results on an in-house and two public datasets validate the effectiveness of our method. Impact: Federated MR image reconstruction promotes collaboration across multiple institutions and effectively leverages data from different organizations to enhance model performance, while mitigating privacy concerns.