  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.
