Multi-institutional efforts can facilitate training of deep MRI reconstruction models, albeit privacy risks arise during cross-site sharing of imaging data. Federated learning (FL) has recently been introduced to address privacy concerns by enabling distributed training without transfer of imaging data. Existing FL methods for MRI reconstruction employ conditional models to map from undersampled to fully-sampled acquisitions via explicit knowledge of the imaging operator. Since conditional models generalize poorly across different acceleration rates or sampling densities, imaging operators must be fixed between training and testing, and they are typically matched across sites. To improve generalization and flexibility in multi-institutional collaborations, here we introduce a novel method for MRI reconstruction based on Federated learning of Generative IMage Priors (FedGIMP). FedGIMP leverages a two-stage approach: cross-site learning of a generative MRI prior, and subject-specific injection of the imaging operator. The global MRI prior is learned via an unconditional adversarial model that synthesizes high-quality MR images based on latent variables. Specificity in the prior is preserved via a mapper subnetwork that produces site-specific latents. During inference, the prior is combined with subject-specific imaging operators to enable reconstruction, and further adapted to individual test samples by minimizing data-consistency loss. Comprehensive experiments on multi-institutional datasets clearly demonstrate enhanced generalization performance of FedGIMP against site-specific and federated methods based on conditional models, as well as traditional reconstruction methods.
翻译:尽管在图像数据跨地点共享过程中出现隐私风险,但多机构努力可以促进深部磁RI重建模型的培训,尽管在图像数据跨地点共享过程中出现隐私风险。最近引入了联邦学习(FL),以解决隐私问题,方法是使分散式培训能够不转让成像数据。现有FL系统重建方法采用有条件模型,从抽样不足的模型绘制成成像操作员的明显知识,充分标本;由于条件模型在不同的加速率或取样密度之间普遍分布不一,因此成像操作员必须固定在培训和测试之间,而且通常在各地点进行匹配。为了改进多机构合作的普及性和灵活性,我们在此引入了一种新的MRI系统重建方法,其基础是:在对基因化成像前的模型进行跨地点学习,通过对成像操作员进行跨地点学习,对成像操作员进行跨地点学习,通过基于潜在变量的高质量MMSM图像图像综合的无条件对抗模型学习;为了保持以前的特性,我们采用了一种基于具体地点潜在潜力的地图子网络,我们采用了一种新的方法。FedGMP利用两阶段的模型,使具体数据采集模型进行精确的模型进行实地测试,使先前的模型进行精确的模型进行实地测试,从而能够使具体化,使具体化,从而能够使具体地进行实地的模型进行精确地改进。