<ul><li>Deep learning-based 3D imaging, especially MRI, faces challenges due to limited availability of 3D training data.</li><li>Existing methods for 3D MRI reconstruction with 2D diffusion priors suffer from decreased performance when voxel size varies.</li><li>Researchers propose a resolution-robust approach using diffusion-guided regularization of randomly sampled 2D slices.</li><li>Model-based approaches fail to bridge the performance gap, while training the diffusion model on various resolutions provides a resolution-robust method without compromising accuracy.</li></ul>

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

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