Physics driven reduced order model for real time blood flow simulations

TL;DR

This paper introduces a physics-driven reduced order model that enables real-time, accurate blood flow simulations for clinical applications by efficiently predicting hemodynamic changes based on patient-specific geometries.

Contribution

The authors develop a novel reduced order modeling approach tailored to individual patient geometries, significantly speeding up blood flow predictions compared to traditional CFD methods.

Findings

Achieved a correlation coefficient of 0.98 with CFD simulations.

Validated on over 1300 patients with low error margins.

Enabled real-time prediction of fractional flow reserve changes.

Abstract

Predictive modeling of blood flow and pressure have numerous applications ranging from non-invasive assessment of functional significance of disease to planning invasive procedures. While several such predictive modeling techniques have been proposed, their use in the clinic has been limited due in part to the significant time required to perform virtual interventions and compute the resultant changes in hemodynamic conditions. We propose a fast hemodynamic assessment method based on first constructing an exploration space of geometries, tailored to each patient, and subsequently building a physics driven reduced order model in this space. We demonstrate that this method can predict fractional flow reserve derived from coronary computed tomography angiography in response to changes to a patient-specific lumen geometry in real time while achieving high accuracy when compared to computational fluid dynamics simulations. We validated this method on over 1300 patients that received a coronary CT scan and demonstrated a correlation coefficient of 0.98 with an error of 0.005 +- 0.015 (95% confidence interval: (-0.020, 0.031)) as compared to three-dimensional blood flow calculations.