Multi-institutional collaborations are key for learning generalizable MRI synthesis models that translate source- onto target-contrast images. To facilitate collaboration, federated learning (FL) adopts decentralized training and mitigates privacy concerns by avoiding sharing of imaging data. However, FL-trained synthesis models can be impaired by the inherent heterogeneity in the data distribution, with domain shifts evident when common or variable translation tasks are prescribed across sites. Here we introduce the first personalized FL method for MRI Synthesis (pFLSynth) to improve reliability against domain shifts. pFLSynth is based on an adversarial model that produces latents specific to individual sites and source-target contrasts, and leverages novel personalization blocks to adaptively tune the statistics and weighting of feature maps across the generator stages given latents. To further promote site specificity, partial model aggregation is employed over downstream layers of the generator while upstream layers are retained locally. As such, pFLSynth enables training of a unified synthesis model that can reliably generalize across multiple sites and translation tasks. Comprehensive experiments on multi-site datasets clearly demonstrate the enhanced performance of pFLSynth against prior federated methods in multi-contrast MRI synthesis.
翻译:多机构协作是学习将源转化为目标 contrastrast 图像的通用MRI合成模型的关键。为了促进协作,联邦学习(FL)采用分散化培训,并通过避免分享成象数据来减少隐私问题。然而,FL培训的合成模型可能因数据分布的内在异质性而受损,当各站点指定共同或可变翻译任务时,就明显地进行域变换。在这里,我们为MRI合成(pFLSynth)引入了第一种个性化FLL法,以提高对域变换的可靠性。pFLSynth基于一种对抗性模型,该模型产生各个站点和源目标对比的潜值,并利用新的个性化元件来适应性调整生成者各阶段具有潜值的地貌图的统计和加权。为了进一步促进地点的特殊性,部分模型汇总在发电机下游层上使用,同时保留上游层。因此,pLFLSynth能够培训一个能够可靠地跨越多个站点和翻译任务的统一合成模型。在多站点数据集上进行的全面实验,明确显示多功能合成合成的强化性合成方法。