标题： 更大的并不总是更好：面向医学图像重建的通用先验 显示英文标题

标题： Bigger Isn't Always Better: Towards a General Prior for Medical Image Reconstruction

摘要： 扩散模型已被成功应用于许多逆问题，包括磁共振成像和计算机断层扫描重建。 研究人员通常会重新使用最初为无条件采样设计的模型而无需修改。 使用两种不同的后验采样算法，我们实证表明这样的大型网络并非必要。 我们最小的模型，实际上是一个ResNet，在分布内重建中表现几乎与注意力U-Net相当，同时在分布偏移方面具有显著的鲁棒性。 此外，我们引入了在自然图像上训练的模型，并证明它们可以用于磁共振成像和计算机断层扫描重建，在分布外情况下表现优于在医学图像上训练的模型。 基于我们的发现，我们强烈警告不要简单地重复使用非常大的网络，并鼓励研究人员根据各自任务调整模型复杂度。 此外，我们认为，迈向通用扩散基础先验的关键步骤是在自然图像上进行训练。 显示英文摘要

摘要： Diffusion model have been successfully applied to many inverse problems, including MRI and CT reconstruction. Researchers typically re-purpose models originally designed for unconditional sampling without modifications. Using two different posterior sampling algorithms, we show empirically that such large networks are not necessary. Our smallest model, effectively a ResNet, performs almost as good as an attention U-Net on in-distribution reconstruction, while being significantly more robust towards distribution shifts. Furthermore, we introduce models trained on natural images and demonstrate that they can be used in both MRI and CT reconstruction, out-performing model trained on medical images in out-of-distribution cases. As a result of our findings, we strongly caution against simply re-using very large networks and encourage researchers to adapt the model complexity to the respective task. Moreover, we argue that a key step towards a general diffusion-based prior is training on natural images.