BrainNormalizer: anatomy-informed pseudo-healthy brain reconstruction from tumor MRI via edge-guided ControlNet

作者：Min Gu Kwak, Yeon‐Ju Lee, Hairong Wang, Kristin R. Swanson, Jing Li · 发表于：IISE Transactions on Healthcare Systems Engineering · 年份：2026 · DOI：10.1080/24725579.2026.2699157 · 研究领域：Generative Adversarial Networks and Image Synthesis、Glioma Diagnosis and Treatment、Functional Brain Connectivity Studies

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. ...