Imputation of missing images via source-to-target modality translation can improve diversity in medical imaging protocols. A pervasive approach for synthesizing target images involves one-shot mapping through generative adversarial networks (GAN). Yet, GAN models that implicitly characterize the image distribution can suffer from limited sample fidelity. Here, we propose a novel method based on adversarial diffusion modeling, SynDiff, for improved performance in medical image translation. To capture a direct correlate of the image distribution, SynDiff leverages a conditional diffusion process that progressively maps noise and source images onto the target image. For fast and accurate image sampling during inference, large diffusion steps are taken with adversarial projections in the reverse diffusion direction. To enable training on unpaired datasets, a cycle-consistent architecture is devised with coupled diffusive and non-diffusive modules that bilaterally translate between two modalities. Extensive assessments are reported on the utility of SynDiff against competing GAN and diffusion models in multi-contrast MRI and MRI-CT translation. Our demonstrations indicate that SynDiff offers quantitatively and qualitatively superior performance against competing baselines.

翻译：通过源到目标模态翻译对缺失图像进行填补，可提升医学成像协议的多样性。一种广泛使用的目标图像合成方法是基于生成对抗网络（GAN）的一次性映射。然而，隐式表征图像分布的GAN模型可能面临样本保真度有限的问题。本文提出一种基于对抗扩散建模的新方法SynDiff，用于改进医学图像翻译性能。为捕获图像分布的直接相关性，SynDiff利用条件扩散过程逐步将噪声和源图像映射至目标图像。在推理阶段，为快速准确采样图像，采用反向扩散方向上的对抗投影实现大步长扩散。为实现非配对数据集的训练，设计了一种循环一致性架构，包含耦合的扩散与非扩散模块，可实现两种模态间的双向翻译。针对SynDiff与竞争性GAN及扩散模型在多对比度MRI及MRI-CT翻译中的效用，本文进行了广泛评估。实验表明，SynDiff在定量和定性性能上均优于竞争性基线模型。