  Targeted diagnosis and treatment plans for patients with coronary artery
disease vary according to atherosclerotic plaque component. Coronary CT
angiography (CCTA) is widely used for artery imaging and determining the
stenosis degree. However, the limited spatial resolution and susceptibility to
artifacts fail CCTA in obtaining lumen morphological characteristics and plaque
composition. It can be settled by invasive optical coherence tomography (OCT)
without much trouble for physicians, but bringing higher costs and potential
risks to patients. Therefore, it is clinically critical to introduce
annotations of plaque tissue and lumen characteristics from OCT to paired CCTA
scans, denoted as \textbf{the O2CTA problem} in this paper. We propose a method
to handle the O2CTA problem. CCTA scans are first reconstructed into
multi-planar reformatted (MPR) images, which agree with OCT images in term of
semantic contents. The artery segment in OCT, which is manually labelled, is
then spatially aligned with the entire artery in MPR images via the proposed
alignment strategy. Finally, a classification model involving a 3D CNN and a
Transformer, is learned to extract local features and capture dependence along
arteries. Experiments on 55 paired OCT and CCTA we curate demonstrate that it
is feasible to classify the CCTA based on the OCT labels, with an accuracy of
86.2%, while the manual readings of OCT and CCTA vary significantly, with a
Kappa coefficient of 0.113. We will make our source codes, models, data, and
results publicly available to benefit the research community.
