Curation of large, diverse MRI datasets via multi-institutional collaborations can help improve learning of generalizable synthesis models that reliably translate source- onto target-contrast images. To facilitate collaborations, federated learning (FL) adopts decentralized model training while mitigating privacy concerns by avoiding sharing of imaging data. However, conventional FL methods can be impaired by the inherent heterogeneity in the data distribution, with domain shifts evident within and across imaging sites. Here we introduce the first personalized FL method for MRI Synthesis (pFLSynth) that improves reliability against data heterogeneity via model specialization to individual sites and synthesis tasks (i.e., source-target contrasts). To do this, pFLSynth leverages an adversarial model equipped with novel personalization blocks that control the statistics of generated feature maps across the spatial/channel dimensions, given latent variables specific to sites and tasks. To further promote communication efficiency and site specialization, partial network aggregation is employed over later generator stages while earlier generator stages and the discriminator are trained locally. As such, pFLSynth enables multi-task training of multi-site synthesis models with high generalization performance across sites and tasks. Comprehensive experiments demonstrate the superior performance and reliability of pFLSynth in MRI synthesis against prior federated methods.

中文翻译：

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一种将它们统一起来的模型：多对比 MRI 合成的个性化联合学习

通过多机构合作管理大型、多样化的 MRI 数据集可以帮助提高对可概括的合成模型的学习，这些模型能够可靠地将源对比图像转换为目标对比图像。为了促进协作，联邦学习（FL）采用分散式模型训练，同时通过避免共享成像数据来减轻隐私问题。然而，传统的 FL 方法可能会受到数据分布中固有的异质性的影响，成像部位内部和之间的域变化很明显。在这里，我们介绍了第一个用于 MRI 合成的个性化 FL 方法 (pFLSynth)，该方法通过针对各个站点和合成任务（即源目标对比）的模型专业化来提高针对数据异质性的可靠性。为此，pFLSynth 利用配备新颖个性化模块的对抗模型，在给定特定于站点和任务的潜在变量的情况下，控制跨空间/通道维度生成的特征图的统计数据。为了进一步提高通信效率和站点专业化，在后面的生成器阶段采用部分网络聚合，而早期的生成器阶段和鉴别器则在本地进行训练。因此，pFLSynth 能够对多站点综合模型进行多任务训练，并具有跨站点和任务的高泛化性能。综合实验证明，与之前的联合方法相比，pFLSynth 在 MRI 合成中具有卓越的性能和可靠性。