标题： 基于二维扩散先验的分辨率稳健三维MRI重建：多样化分辨率训练优于插值 显示英文标题

标题： Resolution-Robust 3D MRI Reconstruction with 2D Diffusion Priors: Diverse-Resolution Training Outperforms Interpolation

摘要： 基于深度学习的三维成像，特别是磁共振成像（MRI），由于三维训练数据的可用性有限而具有挑战性。因此，开始利用在二维切片上训练的二维扩散模型来进行三维MRI重建。然而，正如本文所示，现有方法适用于固定的体素大小，当体素大小变化时性能会下降，这在临床实践中是常见的。在本文中，我们提出了并研究了几种基于二维扩散先验的分辨率鲁棒的三维MRI重建方法。通过这项研究，我们获得了一种基于随机采样的二维切片的扩散引导正则化的简单分辨率鲁棒的变分三维重建方法。与后验采样基线方法相比，该方法提供了具有竞争力的重建质量。为了解决对分辨率变化的敏感性，我们研究了最先进的基于模型的方法，包括高斯点云、神经表示和无限维扩散模型，以及一种在多个分辨率上训练扩散模型的简单数据驱动方法。我们的实验表明，基于模型的方法无法在三维MRI中缩小性能差距。相反，在不同分辨率上训练扩散模型的数据驱动方法有效地提供了一种分辨率鲁棒的方法，而不会牺牲准确性。 显示英文摘要

摘要： Deep learning-based 3D imaging, in particular magnetic resonance imaging (MRI), is challenging because of limited availability of 3D training data. Therefore, 2D diffusion models trained on 2D slices are starting to be leveraged for 3D MRI reconstruction. However, as we show in this paper, existing methods pertain to a fixed voxel size, and performance degrades when the voxel size is varied, as it is often the case in clinical practice. In this paper, we propose and study several approaches for resolution-robust 3D MRI reconstruction with 2D diffusion priors. As a result of this investigation, we obtain a simple resolution-robust variational 3D reconstruction approach based on diffusion-guided regularization of randomly sampled 2D slices. This method provides competitive reconstruction quality compared to posterior sampling baselines. Towards resolving the sensitivity to resolution-shifts, we investigate state-of-the-art model-based approaches including Gaussian splatting, neural representations, and infinite-dimensional diffusion models, as well as a simple data-centric approach of training the diffusion model on several resolutions. Our experiments demonstrate that the model-based approaches fail to close the performance gap in 3D MRI. In contrast, the data-centric approach of training the diffusion model on various resolutions effectively provides a resolution-robust method without compromising accuracy.