Transformer-Based Prediction of Coronary Artery Lumen Expansion Post Angioplasty Using Optical Coherence Tomography

Abstract

Coronary artery disease is the leading cause of mortality globally, resulting in an urgent and critical need to better understand both vessel morphology and the processes of intervention. Angioplasty is an intervention which causes a previously constricted vessel to expand via placement of a stent, and is affected by numerous characteristics of the vessel such as calcium eccentricity and size, wall thickness, and prior lumen size. Being able to accurately assess whether a stent will properly expand allows cardiologists to pursue pre-stenting calcium lesion modification strategies that help avoid dangerous complications of improper stenting. This work introduces a pipeline for post-stenting lumen area prediction from pre-stenting optical coherence tomography (OCT) images. This pipeline includes morphological correction of OCT image segmentations, explainable feature extraction from OCT segmentations, and a predictive transformer network that combines morphological features with injected stent information. The aim is for such a pipeline to be used to support clinical decision making.