用分层扩散模型提升脑部MRI修复的三维一致性，兼顾效率与质量。
Hierarchical Diffusion Framework for Pseudo-Healthy Brain MRI Inpainting with Enhanced 3D Consistency
- 分两阶段用轴向与冠状面2D扩散模型，逐步修复图像
- 在公开数据集上达到最高视觉真实感和体积连续性评分
- 适合医疗影像预处理，尤其数据有限的场景
伪健康图像修复是分析病理性脑部MRI的重要预处理步骤。现有方法多采用切片级2D模型以保证平面内保真度，但各切片间独立导致体数据不连续。全3D模型虽可缓解此问题，却需大量训练数据，医疗场景中难实现。本文提出一种分层扩散框架，将直接3D建模替换为两个垂直方向的粗到精2D阶段：先由轴向扩散模型生成全局一致的粗略修复；再由冠状向扩散模型细化解剖细节。通过结合垂直空间视角与自适应重采样，该方法在数据效率与体积一致性间取得平衡。实验表明，本方法在真实感和体积连续性上均优于当前最优基线，是伪健康图像修复的有力方案。代码已开源：https://github.com/dou0000/3dMRI-Consistent-Inpaint。
原文摘要 · Abstract (English)
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.
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