EMBER: Learning Hidden Lesion Dynamics from Intermittent Brain MRI

Progression independent of relapse activity is a major source of disability in multiple sclerosis, yet routine brain MRI reveals lesion evolution only at intermittent visits. Assigning a lesion's birth to its detection date creates an artificial chronology; unobserved disease activity can also make unrelated regional lesions appear mutually exciting. We introduce EMBER, a learning framework that integrates interval-censored lesion histories with hidden Hawkes dynamics and longitudinal tissue measurements. Its first contribution is an observation-compatible branching representation that marginalizes lesion timing and latent common activity while allowing edge-specific mixtures of temporal kernels. Its second is a posterior-refinement objective: after scans are removed, the predicted risk must equal an average over compatible refinements, rather than remain identical to the prediction from one richer history. We establish exact coarsening identities and a Brier-risk decomposition that characterize the information lost through intermittent acquisition. Open longitudinal MRI tests lesion-change forecasting. In the clinical evaluation, EMBER predicts 24-month relapse-independent progression with held-out-site AUROC 0.846 and Brier score 0.123, and maintains discrimination under reduced scan availability. The resulting model links uncertain lesion chronology to clinically meaningful progression risk using routine T1-weighted and FLAIR MRI.