E2AD: Enhanced and explainable Alzheimer's disease detection framework via anatomy- and relation-aware cross-modal knowledge distillation.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder for which MRI and PET provide complementary structural and molecular information. Yet PET remains costly and often unavailable, motivating MRI-only diagnostic systems that still benefit from multimodal supervision. Existing methods either synthesize PET from MRI or limit cross-modal learning to low-dimensional spaces, underutilizing MRI-PET complementarity and leading to limited robustness and generalizability. To address these challenges, we introduce E2AD, an Enhanced and Explainable AD detection framework that leverages anatomy- and relation-aware cross-modal knowledge distillation (KD). Using paired MRI-PET data during training but only MRI at inference, E2AD augments traditional logit-based KD through two synergistic components: (1) anatomy-aware distillation that transfers within-subject anatomical dependencies through an anatomical Mixture-of-Mappers, yielding spatially meaningful and clinically traceable cues; and (2) relation-aware distillation that promotes stable between-subject structural relations through generalizable pairwise alignment, yielding a representation space with better cross-cohort generalization. To enhance clinical utility, we further introduce a tailored multi-agent workflow that translates E2AD's anatomical attention into structured, clinician-oriented MRI reports. Extensive experimental results on the internal ADNI cohort and two external cohorts (AIBL and NACC) demonstrate that E2AD outperforms state-of-the-art baselines, offering faster convergence, improved data efficiency, stronger cross-cohort generalization, and enhanced explainability. Source code is available at https://github.com/thibault-wch/E2AD-for-Alzheimer-disease.