DuoPIC: Uncertainty-Aware Voxel-Level Digital Twins of Cerebral Perfusion with Transport PDEs in Acute Stroke

Researchers introduced DuoPIC, a digital twin of cerebral perfusion that couples a Windkessel flow model to a tracer transport partial differential equation with a population-level neural closure, requiring no reference maps. The voxel-level maps set state-of-the-art lesion detection on ISLES'17 MR (AUC 0.745) and ISLES'24 CT (AUC 0.743), and on held-out patients the neural PDE cut the compartment's reconstruction error by 19% while lifting 95% MTT interval coverage from 88% to 94%. Code is available at github.com/detinatran/duopic.

arXiv:2610.33071v1 Announce Type: new Abstract: Digital twins of cerebral perfusion relate tracer dynamics to cerebral blood flow CBF , blood volume CBV and mean transit time MTT , but must be data-driven, physically consistent and uncertainty-aware. We introduce DuoPIC, a digital twin that needs no reference maps, in which a Windkessel flow model drives a tracer transport partial differential equation PDE . A population-level neural closure divides each region into fast and slow pathways with tracer mass and MTT conserved by construction, and a tempered posterior quantifies the uncertainty of every estimate. Its voxel maps set the state of the art for lesion detection on ISLES'17 MR AUC 0.745 and ISLES'24 CT 0.743 . On held-out patients, the neural PDE reduced the compartment's reconstruction error by 19% and lifted 95% MTT interval coverage from 88% to 94%. Code: https://github.com/detinatran/duopic