Multi-center positron emission tomography (PET) image synthesis aims at recovering low-dose PET images from multiple different centers. The generalizability of existing methods can still be suboptimal for a multi-center study due to domain shifts, which result from non-identical data distribution among centers with different imaging systems/protocols. While some approaches address domain shifts by training specialized models for each center, they are parameter inefficient and do not well exploit the shared knowledge across centers. To address this, we develop a generalist model that shares architecture and parameters across centers to utilize the shared knowledge. However, the generalist model can suffer from the center interference issue, \textit{i.e.} the gradient directions of different centers can be inconsistent or even opposite owing to the non-identical data distribution. To mitigate such interference, we introduce a novel dynamic routing strategy with cross-layer connections that routes data from different centers to different experts. Experiments show that our generalist model with dynamic routing (DRMC) exhibits excellent generalizability across centers. Code and data are available at: https://github.com/Yaziwel/Multi-Center-PET-Image-Synthesis.

翻译：摘要：多中心正电子发射断层扫描（PET）图像合成旨在从多个不同中心恢复低剂量PET图像。由于不同中心采用不同的成像系统/协议，导致数据分布不一致，从而产生领域偏移，这使得现有方法的泛化能力在多中心研究中仍可能欠佳。尽管一些方法通过为每个中心训练专门模型来解决领域偏移问题，但这些方法参数效率低下且未能充分利用跨中心的共享知识。为此，我们开发了一个跨中心共享架构和参数的通用模型以利用共享知识。然而，通用模型可能面临中心干扰问题（即不同中心的梯度方向因数据分布不一致而存在矛盾甚至对立）。为缓解此类干扰，我们创新性地提出了一种带有跨层连接的动态路由策略，可将不同中心的数据路由到不同的专家模块。实验表明，我们提出的基于动态路由的通用模型（DRMC）在跨中心任务中展现出卓越的泛化能力。代码与数据公开于：https://github.com/Yaziwel/Multi-Center-PET-Image-Synthesis。