Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction
This addresses a practical challenge in federated learning for medical imaging, enabling multi-modal reconstruction in real clinical settings where data is incomplete, though it is incremental as it builds on existing federated and multi-modal methods.
The paper tackles the missing modality problem in federated multi-modal MRI reconstruction, where medical institutions often have only single-modal data, by proposing Fed-PMG, a communication-efficient framework that uses pseudo modality generation and clustering to recover missing modalities, achieving performance similar to the ideal scenario with full modalities.
While multi-modal learning has been widely used for MRI reconstruction, it relies on paired multi-modal data which is difficult to acquire in real clinical scenarios. Especially in the federated setting, the common situation is that several medical institutions only have single-modal data, termed the modality missing issue. Therefore, it is infeasible to deploy a standard federated learning framework in such conditions. In this paper, we propose a novel communication-efficient federated learning framework, namely Fed-PMG, to address the missing modality challenge in federated multi-modal MRI reconstruction. Specifically, we utilize a pseudo modality generation mechanism to recover the missing modality for each single-modal client by sharing the distribution information of the amplitude spectrum in frequency space. However, the step of sharing the original amplitude spectrum leads to heavy communication costs. To reduce the communication cost, we introduce a clustering scheme to project the set of amplitude spectrum into finite cluster centroids, and share them among the clients. With such an elaborate design, our approach can effectively complete the missing modality within an acceptable communication cost. Extensive experiments demonstrate that our proposed method can attain similar performance with the ideal scenario, i.e., all clients have the full set of modalities. The source code will be released.