Anisotropic low-resolution (LR) magnetic resonance (MR) images are fast to obtain but hinder automated processing. We propose to use denoising diffusion probabilistic models (DDPMs) to super-resolve these 2D-acquired LR MR slices. This paper introduces AniRes2D, a novel approach combining DDPM with a residual prediction for 2D super-resolution (SR). Results demonstrate that AniRes2D outperforms several other DDPM-based models in quantitative metrics, visual quality, and out-of-domain evaluation. We use a trained AniRes2D to super-resolve 3D volumes slice by slice, where comparative quantitative results and reduced skull aliasing are achieved compared to a recent state-of-the-art self-supervised 3D super-resolution method. Furthermore, we explored the use of noise conditioning augmentation (NCA) as an alternative augmentation technique for DDPM-based SR models, but it was found to reduce performance. Our findings contribute valuable insights to the application of DDPMs for SR of anisotropic MR images.

翻译：各向异性低分辨率磁共振图像虽可快速获取，但会阻碍自动化处理进程。我们提出采用去噪扩散概率模型对二维采集的各向异性低分辨率磁共振切片进行超分辨率重建。本文介绍了一种创新方法AniRes2D，该模型结合了去噪扩散概率模型与残差预测机制，专门用于实现二维超分辨率重建。实验结果表明，AniRes2D在定量指标、视觉质量及域外评估中均优于多个基于去噪扩散概率模型的对比方法。我们采用训练好的AniRes2D逐切片对三维体数据进行超分辨率重建，与近期最优的自监督三维超分辨率方法相比，获得了更优的定量比较结果，并有效减轻了颅骨伪影。此外，我们探索了将噪声条件增强作为去噪扩散概率模型超分辨率模型的替代增强技术，但发现该技术会降低模型性能。本研究为各向异性磁共振图像超分辨率中应用去噪扩散概率模型提供了重要见解。