联邦学习实现多中心脑部MRI转CT，保护隐私同时提升模型泛化能力。
FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis
- 采用跨数据中心联邦学习框架，联合训练U-Net模型生成sCT图像。
- 在未参与训练的中心测试中，平均误差仅102.0 HU，结构相似性达0.89。
- 适合需跨机构协作且注重数据隐私的医学影像合成任务。
合成计算机断层扫描（sCT）图像已成为现代临床实践中的关键方法，尤其在放射治疗（RT）规划中具有重要意义。利用sCT可进行剂量计算，推动磁共振成像（MRI）引导放疗的发展。基于深度学习的MRI转sCT方法表现良好，但其依赖单中心数据集，难以泛化至多样临床场景。集中式多中心数据集的构建可能引发隐私问题。为此，我们提出FedSynthCT-Brain，一种基于联邦学习（FL）的脑部MRI转sCT框架。这是首个将联邦学习应用于该任务的研究，采用跨孤岛水平联邦学习方式，允许多家机构协作训练基于U-Net的深度学习模型。我们在来自欧洲和美国四家中心的真实多中心数据上验证了该方法，模拟了不同扫描仪类型和采集模态的异质性，并在联邦外独立中心的数据集上进行测试。在未见中心的测试中，联邦模型在23名患者上的中位均方误差为102.0 HU，四分位距为96.7–110.5 HU；结构相似性指数（SSIM）中位数为0.89（0.86–0.89），峰值信噪比（PNSR）中位数为26.58（25.52–27.42）。结果表明联邦学习方法性能可接受，展示了其提升MRI转sCT泛化能力、推动安全公平临床应用的潜力，同时促进协作并保护数据隐私。
原文摘要 · Abstract (English)
The generation of Synthetic Computed Tomography (sCT) images has become a pivotal methodology in modern clinical practice, particularly in the context of Radiotherapy (RT) treatment planning. The use of sCT enables the calculation of doses, pushing towards Magnetic Resonance Imaging (MRI) guided radiotherapy treatments. Deep learning methods for MRI-to-sCT have shown promising results, but their reliance on single-centre training dataset limits generalisation capabilities to diverse clinical settings. Moreover, creating centralised multi-centre datasets may pose privacy concerns. To address the aforementioned issues, we introduced FedSynthCT-Brain, an approach based on the Federated Learning (FL) paradigm for MRI-to-sCT in brain imaging. This is among the first applications of FL for MRI-to-sCT, employing a cross-silo horizontal FL approach that allows multiple centres to collaboratively train a U-Net-based deep learning model. We validated our method using real multicentre data from four European and American centres, simulating heterogeneous scanner types and acquisition modalities, and tested its performance on an independent dataset from a centre outside the federation. In the case of the unseen centre, the federated model achieved a median Mean Absolute Error (MAE) of $102.0$ HU across 23 patients, with an interquartile range of $96.7-110.5$ HU. The median (interquartile range) for the Structural Similarity Index (SSIM) and the Peak Signal to Noise Ratio (PNSR) were $0.89 (0.86-0.89)$ and $26.58 (25.52-27.42)$, respectively. The analysis of the results showed acceptable performances of the federated approach, thus highlighting the potential of FL to enhance MRI-to-sCT to improve generalisability and advancing safe and equitable clinical applications while fostering collaboration and preserving data privacy.
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