Stent detection with very thick tissue coverage in intravascular OCT

Coronary scenting or percutaneous coronary intervention (PCI) is widely used to treat coronary artery disease. Improper deployment of stents may lead to post-PCI complication, in-stent restenosis, stent fracture and stent thrombosis. Intravascular optical coherence tomography (OCT) with micron-scale resolution provides accurate in vivo assessment of stent apposition/malapposition and neointima coverage. However, manual stent analysis is labor intensive and time consuming. Existing automated methods with intravascular OCT mainly focused on stent struts with thin tissue coverage. We developed a deep learning method to automatically analyze stents with both thin (<= 0.3mm) and very thick tissue coverage (>0.3mm), and an algorithm to accurately analyze stent area for vessels with multiple stents. 25203 images from 56 OCT pullbacks and 41 patients were analyzed. Three-fold cross-validation demonstrated that the algorithm achieved a precision of 0.932*0.009 and a sensitivity of 0.939*0.007 for stents with <= 0.3mm tissue coverage, and a precision of 0.856 +/- 0.019 and a sensitivity of 0.874 +/- 0.011 for stents with >0.3mm tissue coverage. The correlation between the automatically computed and manually measured stent area is 0.954 (p<0.0001) for vessels with a single stent, and is 0.918 (p<0.0001) for vessels implanted with multiple stents. The proposed method can accurately detect stent struts with very thick tissue coverage and analyze stent area in vessels implanted with multiple stents, and can effectively facilitate the evaluation of stent implantation and post-stent tissue coverage. (C) 2021 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

This study developed a deep learning method to automatically analyze stents with different thicknesses and vessels implanted with multiple stents, providing effective assistance for the evaluation of stent implantation and post-stent tissue coverage.