BrainNormalizer：通过边缘引导控制网络从肿瘤MRI进行解剖学信息伪健康脑重建
BrainNormalizer: Anatomy-Informed Pseudo-Healthy Brain Reconstruction from Tumor MRI via Edge-Guided ControlNet
AI总结：
针对脑肿瘤致结构变形难区分肿瘤与解剖变异问题，提出BrainNormalizer框架，用两阶段训练学习解剖先验等，通过特定策略实现伪健康脑重建，实验表明其有优势。
摘要：
脑肿瘤会引起复杂的结构变形，掩盖患者原来的神经解剖结构，难以区分肿瘤引起的变化和固有的解剖变异。重建特定个体的伪健康脑可为这种分析提供关键参考，但此任务本质上是反事实的，因为没有配对的肿瘤前扫描和明确的健康指导。我们提出了BrainNormalizer，这是一个基于扩散的框架，用于特定个体的伪健康脑MRI重建，能够在不需要配对数据或明确健康参考的情况下进行解剖学信息重建。该框架通过一个由基于修复的扩散微调的两阶段训练策略和基于ControlNet的边缘条件来学习解剖学先验和基于边缘的结构条件。在推理时，通过故意错位策略实现反事实伪健康重建，其中肿瘤输入与非肿瘤提示和镜像对侧边缘图配对。这允许从患者自身的解剖结构构建特定个体的解剖学指导，从而实现保留个体结构特征的解剖学一致的伪健康重建。在BraTS2020数据集上的实验表明，与现有方法相比，BrainNormalizer在分布逼真度、基于对称性的结构一致性和减少假阳性检测方面取得了改进。这些结果表明，所提出的框架为特定个体的反事实重建提供了一种有原则的方法，并支持肿瘤引起的变形的下游分析。
英文摘要：
Brain tumors induce complex structural deformations that obscure the patient' s original neuroanatomy, making it difficult to distinguish tumor-induced changes from inherent anatomical variability. Reconstructing a subject-specific pseudo-healthy brain can provide a critical reference for such analysis, but this task is inherently counterfactual, as paired pre-tumor scans and explicit healthy guidance are unavailable. We propose BrainNormalizer, a diffusion-based framework for subject-specific pseudo-healthy brain MRI reconstruction that enables anatomy-informed reconstruction without requiring paired data or explicit healthy references. The framework learns anatomical priors and edge-based structural conditioning through a two-stage training strategy consisting of inpainting-based diffusion fine-tuning and ControlNet-based edge conditioning. At inference, counterfactual pseudo-healthy reconstruction is achieved through a deliberate misalignment strategy, where tumorous inputs are paired with non-tumorous prompts and mirrored contralateral edge maps. This allows subject-specific anatomical guidance to be constructed from the patient's own anatomy, enabling anatomically consistent pseudo-healthy reconstruction that preserves individual structural characteristics. Experiments on the BraTS2020 dataset demonstrate that BrainNormalizer achieves improved distributional realism, symmetry-based structural consistency, and reduced false positive detection compared to existing methods. These results indicate that the proposed framework provides a principled approach for subject-specific counterfactual reconstruction and supports downstream analysis of tumor-induced deformation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
