标题： 基于增强3D一致性的伪健康脑MRI修复的分层扩散框架 显示英文标题

标题： Hierarchical Diffusion Framework for Pseudo-Healthy Brain MRI Inpainting with Enhanced 3D Consistency

摘要： 伪健康图像修复是分析病理性脑部MRI扫描的重要预处理步骤。 大多数当前的修复方法倾向于切片级的2D模型，因为它们具有高平面内保真度，但切片之间的独立性会在体积中产生不连续性。 全3D模型可以缓解这个问题，但它们的高模型容量需要大量的训练数据来进行可靠且高保真的合成——这在医疗环境中通常不现实。 我们通过将直接的3D建模替换为两个垂直的从粗到细的2D阶段，提出了一种分层扩散框架。 一个轴向扩散模型首先生成一个粗略且全局一致的修复；然后一个冠状扩散模型细化解剖细节。 通过结合垂直的空间视角和自适应重采样，我们的方法在数据效率和体积一致性之间取得了平衡。 我们的实验表明，我们的方法在真实感和体积一致性方面都优于最先进的基线，使其成为伪健康图像修复的一种有前景的解决方案。 代码可在 https://github.com/dou0000/3dMRI-Consistent-Inpaint 获取。 显示英文摘要

摘要： 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 https://github.com/dou0000/3dMRI-Consistent-Inpaint.