HemoPT: Flow-State Proxy-Supervised Pretraining on Heterogeneous Vascular Geometries

Predicting vascular hemodynamic fields directly from geometry can reduce the computational cost of performing computational fluid dynamics (CFD) simulations across numerous vascular cases. However, training such surrogate models remains constrained by a substantial imbalance in supervision, with abundant unpaired vascular geometries but scarce and costly paired geometry-flow data. In this paper, we introduce HemoPT, a flow-state proxy-supervised vascular pretraining framework that requires no paired CFD fields. We construct a multi-fidelity, multi-anatomy pretraining corpus comprising 5,310 vascular geometries. By sampling 20 probe conditions per geometry, the corpus yields more than 100,000 probe-conditioned pretraining instances. HemoPT establishes spatial awareness of the vascular lumen through directional probes. This probe-derived spatial context parameterizes analytic responses associated with representative vascular flow states, producing geometry-specific hemodynamic proxies. The resulting geometry-proxy pairs are used to pretrain a Transolver encoder, which is subsequently fine-tuned for downstream velocity-field regression. A controlled study on the Aneumo dataset shows that pretraining with geometry-proxy pairs outperforms conditioning-only and geometry-oriented pretraining settings. Across four downstream tasks and varying data regimes, HemoPT achieves highly competitive performance among all compared training and pretraining methods. On the Vascular Model Repository (VMR) dataset, the gains from pretraining vary with vascular anatomy. These results indicate that HemoPT learns transferable geometry-flow representations from heterogeneous vascular geometries, with transfer benefits shaped by downstream anatomy. Code and preprocessing scripts are available at: https://anonymous.4open.science/r/HemoPT-66CF/