  In patients with obstructive coronary artery disease, the functional
significance of a coronary artery stenosis needs to be determined to guide
treatment. This is typically established through fractional flow reserve (FFR)
measurement, performed during invasive coronary angiography (ICA). We present a
method for automatic and non-invasive detection of patients requiring ICA,
employing deep unsupervised analysis of complete coronary arteries in cardiac
CT angiography (CCTA) images. We retrospectively collected CCTA scans of 187
patients, 137 of them underwent invasive FFR measurement in 192 different
coronary arteries. These FFR measurements served as a reference standard for
the functional significance of the coronary stenosis. The centerlines of the
coronary arteries were extracted and used to reconstruct straightened
multi-planar reformatted (MPR) volumes. To automatically identify arteries with
functionally significant stenosis that require ICA, each MPR volume was encoded
into a fixed number of encodings using two disjoint 3D and 1D convolutional
autoencoders performing spatial and sequential encodings, respectively.
Thereafter, these encodings were employed to classify arteries using a support
vector machine classifier. The detection of coronary arteries requiring
invasive evaluation, evaluated using repeated cross-validation experiments,
resulted in an area under the receiver operating characteristic curve of $0.81
\pm 0.02$ on the artery-level, and $0.87 \pm 0.02$ on the patient-level. The
results demonstrate the feasibility of automatic non-invasive detection of
patients that require ICA and possibly subsequent coronary artery intervention.
This could potentially reduce the number of patients that unnecessarily undergo
ICA.
