  Local hemodynamic forces play an important role in determining the functional
significance of coronary arterial stenosis and understanding the mechanism of
coronary disease progression. Computational fluid dynamics (CFD) have been
widely performed to simulate hemodynamics non-invasively from coronary computed
tomography angiography (CCTA) images. However, accurate computational analysis
is still limited by the complex construction of patient-specific modeling and
time-consuming computation. In this work, we proposed an end-to-end deep
learning framework, which could predict the coronary artery hemodynamics from
CCTA images. The model was trained on the hemodynamic data obtained from 3D
simulations of synthetic and real datasets. Extensive experiments demonstrated
that the predicted hemdynamic distributions by our method agreed well with the
CFD-derived results. Quantitatively, the proposed method has the capability of
predicting the fractional flow reserve with an average error of 0.5\% and 2.5\%
for the synthetic dataset and real dataset, respectively. Particularly, our
method achieved much better accuracy for the real dataset compared to
PointNet++ with the point cloud input. This study demonstrates the feasibility
and great potential of our end-to-end deep learning method as a fast and
accurate approach for hemodynamic analysis.
