用MRI预测阿尔茨海默病淀粉样蛋白，无需昂贵的PET扫描。
Cross-Modal Knowledge Distillation for PET-Free Amyloid-Beta Detection from MRI
- 用跨模态注意力和对比学习，让MRI模仿PET的诊断能力。
- 在两个数据集上最高AUC达0.74，且不依赖临床数据。
- 结果聚焦大脑皮层区域，适合临床筛查场景。
阿尔茨海默病早期诊断中，检测淀粉样蛋白（Aβ）阳性至关重要，但传统方法依赖成本高、侵入性强且普及率低的PET成像，限制了大规模筛查。本文提出一种无PET的跨模态知识蒸馏框架，仅通过MRI即可预测Aβ状态，且推理时不需任何非影像学临床变量或PET图像。该方法采用基于BiomedCLIP的教师模型，利用跨模态注意力与以Centiloid为指导的在线负样本采样的三元组对比学习，实现PET-MRI对齐。学生模型仅使用MRI，通过特征级与逻辑级蒸馏模仿教师。在四种MRI对比度（T1w, T2w, FLAIR, T2*）及两个独立数据集上评估，最佳AUC达0.74（OASIS-3）和0.68（ADNI），同时保持可解释性。显著性分析显示预测关注解剖相关皮层区域，验证其临床可行性。代码已公开于https://github.com/FrancescoChiumento/pet-guided-mri-amyloid-detection。
原文摘要 · Abstract (English)
Detecting amyloid-$β$ (A$β$) positivity is crucial for early diagnosis of Alzheimer's disease but typically requires PET imaging, which is costly, invasive, and not widely accessible, limiting its use for population-level screening. We address this gap by proposing a PET-guided knowledge distillation framework that enables A$β$ prediction from MRI alone, without requiring non-imaging clinical covariates or PET at inference. Our approach employs a BiomedCLIP-based teacher model that learns PET-MRI alignment via cross-modal attention and triplet contrastive learning with PET-informed (Centiloid-aware) online negative sampling. An MRI-only student then mimics the teacher via feature-level and logit-level distillation. Evaluated across four MRI contrasts (T1w, T2w, FLAIR, T2*) and two independent datasets, our approach demonstrates effective knowledge transfer (best AUC: 0.74 on OASIS-3, 0.68 on ADNI) while maintaining interpretability and eliminating the need for clinical variables. Saliency analysis confirms that predictions focus on anatomically relevant cortical regions, supporting the clinical viability of PET-free A$β$ screening. Code is available at https://github.com/FrancescoChiumento/pet-guided-mri-amyloid-detection.
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