FalconBC: Flow matching for Amortized inference of Latent-CONditioned physiologic Boundary Conditions

TL;DR

This paper introduces FalconBC, a flow matching-based amortized inference framework for joint estimation of boundary conditions and patient-specific anatomies in cardiovascular models, addressing limitations of existing methods.

Contribution

FalconBC provides a novel probabilistic flow matching approach for joint inference of boundary conditions and anatomies, improving accuracy in complex cardiovascular modeling scenarios.

Findings

Effective in models with vascular lesions and known flow conditions

Accurately estimates boundary conditions in bifurcation and coronary models

Reduces offline training costs through amortized inference

Abstract

Boundary condition tuning is a fundamental step in patient-specific cardiovascular modeling. Despite an increase in offline training cost, recent methods in data-driven variational inference can efficiently estimate the joint posterior distribution of boundary conditions, with amortization of training efforts over clinical targets. However, even the most modern approaches fall short in two important scenarios: open-loop models with known mean flow and assumed waveform shapes, and anatomies affected by vascular lesions where segmentation influences the reachability of pressure or flow split targets. In both cases, boundary conditions cannot be tuned in isolation. We introduce a general amortized inference framework based on probabilistic flow that treats clinical targets, inflow features, and point cloud embeddings of patient-specific anatomies as either conditioning variables or quantities to be jointly estimated. We demonstrate the approach on two patient-specific models: an aorto-iliac bifurcation with varying stenosis locations and severity, and a coronary arterial tree.