Reconstruction of stented coronary arteries from optical coherence tomography images: Feasibility, validation, and repeatability of a segmentation method

Abstract

Optical coherence tomography (OCT) is an established catheter-based imaging modality for the assessment of coronary artery disease and the guidance of stent placement during percutaneous coronary intervention. Manual analysis of large OCT datasets for vessel contours or stent struts detection is time-consuming and unsuitable for real-time applications. In this study, a fully automatic method was developed for detection of both vessel contours and stent struts. The method was applied to in vitro OCT scans of eight stented silicone bifurcation phantoms for validation purposes. The proposed algorithm comprised four main steps, namely pre-processing, lumen border detection, stent strut detection, and three-dimensional point cloud creation. The algorithm was validated against manual segmentation performed by two independent image readers. Linear regression showed good agreement between automatic and manual segmentations in terms of lumen area (r>0.99). No statistically significant differences in the number of detected struts were found between the segmentations. Mean values of similarity indexes were >95% and >85% for the lumen and stent detection, respectively. Stent point clouds of two selected cases, obtained after OCT image processing, were compared to the centerline points of the corresponding stent reconstructions from micro computed tomography, used as ground-truth. Quantitative comparison between the corresponding stent points resulted in median values of ~150 μm and ~40 μm for the total and radial distances of both cases, respectively. The repeatability of the detection method was investigated by calculating the lumen volume and the mean number of detected struts per frame for seven repeated OCT scans of one selected case. Results showed low deviation of values from the median for both analyzed quantities. In conclusion, this study presents a robust automatic method for detection of lumen contours and stent struts from OCT as supported by focused validation against both manual segmentation and micro computed tomography and by good repeatability.

Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images

“…Developing a patient-specific 3-D model will allow researchers to run structural and fluid dynamics computations within the stented coronary artery. 42 This crucial analysis will provide clinicians with quantitative metrics to assess cardiovascular function postendovascular polymeric scaffold implantation and isolate contributing biomechanical factors. While the current generation of bioresorbable scaffolds has been withdrawn from the market, numerous companies are actively working to develop the next generation of polymeric endovascular implants.…”

Three-dimensional reconstruction and NURBS-based structured meshing of coronary arteries from the conventional X-ray angiography projection images

“…Despite clinical studies, the proposed procedures are suitable for in silico physiology studies, such as virtual calculation of the functional severity of a coronary artery stenosis 44 , 45 , plaque progression 33 and studying CA response to stent implantation 46 , 47 . Our further work will also aim at integrating other imaging techniques, such as intravascular ultrasound 33 and optical coherence tomography 42 , 48 . The complex CA trees were reconstructed by decomposing the tree in branches making the proposed framework easy to be parallelized.…”

A Patient-Specific Study Investigating the Relation between Coronary Hemodynamics and Neo-Intimal Thickening after Bifurcation Stenting with a Polymeric Bioresorbable Scaffold

“…The lumen contours were segmented according to a previously described strategy [6,9]. The pre-processed images were elaborated in polar coordinates and a Sobel filter was applied to detect edges.…”