Abstract
Pseudo-healthy image inpainting is an essential preprocessing step for analyzing pathological brain MRI scans. Most current inpainting methods favor slice-wise 2D models for their high in-plane fidelity, but their independence across slices produces discontinuities in the volume. Fully 3D models alleviate this issue, but their high model capacity demands extensive training data for reliable, high-fidelity synthesis -- often impractical in medical settings. We address these limitations with a hierarchical diffusion framework by replacing direct 3D modeling with two perpendicular coarse-to-fine 2D stages. An axial diffusion model first yields a coarse, globally consistent inpainting; a coronal diffusion model then refines anatomical details. By combining perpendicular spatial views with adaptive resampling, our method balances data efficiency and volumetric consistency. Our experiments show our approach outperforms state-of-the-art baselines in both realism and volumetric consistency, making it a promising solution for pseudo-healthy image inpainting. Code is available at this https URL.
Abstract (translated)
伪健康图像修复是分析病理脑MRI扫描的重要预处理步骤。目前大多数的图像修复方法偏向于使用片状二维模型，因其在平面内的高保真度而被广泛采用；然而，这些模型独立处理每一层会带来体素间不连续性的问题。完全三维的模型可以解决这个问题，但其巨大的模型容量需要大量的训练数据来实现可靠且高质量的合成——这在医学环境中往往是不可行的。我们通过引入一个分层扩散框架解决了这些问题，在该框架中用两个相互垂直的从粗到细二维阶段替代了直接使用三维建模。首先采用轴向（axial）扩散模型进行初步、全局一致性的修复；随后，采用冠状面（coronal）扩散模型来细化解剖细节。通过结合不同空间视角和自适应重采样，我们的方法在数据效率与体素一致性之间取得了平衡。 实验表明，相较于现有的基准方法，我们的方法无论是在逼真度还是体积一致性上都表现更佳，为伪健康图像修复提供了有前景的解决方案。代码可在以下链接获取：[请在此处插入实际URL]。
URL
https://arxiv.org/abs/2507.17911
