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.

使用去噪扩散概率模型对各向异性低分辨率磁共振图像进行超分辨率重建，提出了结合残差预测的AniRes2D新方法。实验结果表明AniRes2D在定量度量、视觉质量和跨域评估等方面优于其他基于DDPM的模型。此外，通过逐层超分辨率重建，使用训练好的AniRes2D减少了头骨伪影，并与最近的自监督3D超分辨率方法进行了对比。此外，我们还探索了噪声条件增强作为DDPM-based SR模型的一种替代增强技术，但发现它会降低性能。我们的研究结果为各向异性磁共振图像的超分辨率重建应用提供了有价值的见解。

AniRes2D：二维 MR 超分辨率的各向异性残差增强扩散