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Targeted diagnosis and treatment plans for patients with coronary arterydisease vary according to atherosclerotic plaque component. Coronary CTangiography (CCTA) is widely used for artery imaging and determining thestenosis degree. However, the limited spatial resolution and susceptibility toartifacts fail CCTA in obtaining lumen morphological characteristics and plaquecomposition. It can be settled by invasive optical coherence tomography (OCT)without much trouble for physicians, but bringing higher costs and potentialrisks to patients. Therefore, it is clinically critical to introduceannotations of plaque tissue and lumen characteristics from OCT to paired CCTAscans, denoted as \textbf{the O2CTA problem} in this paper. We propose a methodto handle the O2CTA problem. CCTA scans are first reconstructed intomulti-planar reformatted (MPR) images, which agree with OCT images in term ofsemantic contents. The artery segment in OCT, which is manually labelled, isthen spatially aligned with the entire artery in MPR images via the proposedalignment strategy. Finally, a classification model involving a 3D CNN and aTransformer, is learned to extract local features and capture dependence alongarteries. Experiments on 55 paired OCT and CCTA we curate demonstrate that itis feasible to classify the CCTA based on the OCT labels, with an accuracy of86.2%, while the manual readings of OCT and CCTA vary significantly, with aKappa coefficient of 0.113. We will make our source codes, models, data, andresults publicly available to benefit the research community.

O2CTA: Introducing Annotations from OCT to CCTA in Coronary Plaque Analysis