3D assessment of stent cell size and side branch access in intravascular optical coherence tomographic pullback runs

Automatic bifurcation detection utilizing pullback characteristics of bifurcation in intravascular optical coherence tomography

Bifurcation detection in coronary arteries is significant since it influences the treatment strategy selection and optimization. Bifurcations are also reliable landmarks for image registration. Intravascular optical coherence tomography (IVOCT) is a high-resolution imaging modality that is very useful in percutaneous coronary intervention stenting optimization. We present a bifurcation identification method utilizing pullback characteristics for IVOCT, which can effectively identify the bifurcations with a small size. The longitudinal view of the pullback will appear as an outward discontinuity in the bifurcation area. By detecting this discontinuity, bifurcation can be identified with high accuracy. We also use the normal vectors method to extract the ostium of bifurcation. We compare the proposed method with the widely-used distance transformation method by clinical 5302 IVOCT images from 22 pullbacks. The average metrics of true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV) for the proposed method are 86.97%, 98.50%, 85.56%, and 98.67%, respectively. TPR, PPV, and NPV by the proposed method are improved by 40.24%, 9.31%, 3.90%, and TNR is on par compared with the distance transformation method. Especially in the small bifurcation identification, TPR of the proposed method is 64.71% higher than the distance transformation method with a bifurcation area ratio less than 0.2.

Current research and future prospects of IVOCT imaging-based detection of the vascular lumen and vulnerable plaque

Intravascular optical coherence tomography (IVOCT) is an imaging method that has developed rapidly in recent years and is useful in coronary atherosclerosis diagnosis. It is widely used in the assessment of vulnerable plaque. This review summarizes the main research methods used in recent years for blood vessel lumen boundary detection and segmentation and vulnerable plaque segmentation and classification. This article aims to comprehensively and systematically introduce the research progress on internal tissues of blood vessels based on IVOCT images. The characteristics and advantages of various methods have been summarized to provide theoretical ideas and methods for the reference of relevant researchers and scholars.

Optical coherence tomography to guide percutaneous treatment of coronary bifurcation disease

INTRODUCTION

Cardiovascular disease remains the most common cause of death worldwide. Enormous progress in the technology and applicability of percutaneous techniques to treat obstructive coronary heart disease has been made, and the number of percutaneous coronary interventions (PCI) is increasing. Coronary bifurcations are involved in a substantial number of PCIs and despite recent advances, bifurcation PCI remains a challenge in terms of immediate success and long-term outcome. Angiography has a limited capacity for showing important features of the 3 dimensional coronary vessel anatomy, position of stent struts and exact wire positions and is therefore suboptimal for guiding bifurcation PCI. Intracoronary optical coherence tomography (OCT) provides high resolution and the information gained during PCI is unprecedented compared with angiography guidance and intravascular ultrasound. Areas covered: This review will provide an overview of the use of OCT to guide bifurcation-PCI. Expert commentary: OCT is a promising guide for bifurcation-PCI at each individual step: from planning the strategy (provisional versus two-stent strategy), to guidance during PCI, and finally checking the interventional result. Until dedicated randomized trails are complete, we recommend OCT guidance for interventions in complex coronary bifurcation disease and for imaging when unexpected procedural events occur.

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

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.

A bifurcation identifier for IV-OCT using orthogonal least squares and supervised machine learning

Intravascular optical coherence tomography (IV-OCT) is an in-vivo imaging modality based on the intravascular introduction of a catheter which provides a view of the inner wall of blood vessels with a spatial resolution of 10-20 μm. Recent studies in IV-OCT have demonstrated the importance of the bifurcation regions. Therefore, the development of an automated tool to classify hundreds of coronary OCT frames as bifurcation or nonbifurcation can be an important step to improve automated methods for atherosclerotic plaques quantification, stent analysis and co-registration between different modalities. This paper describes a fully automated method to identify IV-OCT frames in bifurcation regions. The method is divided into lumen detection; feature extraction; and classification, providing a lumen area quantification, geometrical features of the cross-sectional lumen and labeled slices. This classification method is a combination of supervised machine learning algorithms and feature selection using orthogonal least squares methods. Training and tests were performed in sets with a maximum of 1460 human coronary OCT frames. The lumen segmentation achieved a mean difference of lumen area of 0.11 mm(2) compared with manual segmentation, and the AdaBoost classifier presented the best result reaching a F-measure score of 97.5% using 104 features.

3-D Stent Detection in Intravascular OCT Using a Bayesian Network and Graph Search

Worldwide, many hundreds of thousands of stents are implanted each year to revascularize occlusions in coronary arteries. Intravascular optical coherence tomography is an important emerging imaging technique, which has the resolution and contrast necessary to quantitatively analyze stent deployment and tissue coverage following stent implantation. Automation is needed, as current, it takes up to 16 h to manually analyze hundreds of images and thousands of stent struts from a single pullback. For automated strut detection, we used image formation physics and machine learning via a Bayesian network, and 3-D knowledge of stent structure via graph search. Graph search was done on en face projections using minimum spanning tree algorithms. Depths of all struts in a pullback were simultaneously determined using graph cut. To assess the method, we employed the largest validation data set used so far, involving more than 8000 clinical images from 103 pullbacks from 72 patients. Automated strut detection achieved a 0.91±0.04 recall, and 0.84±0.08 precision. Performance was robust in images of varying quality. This method can improve the workflow for analysis of stent clinical trial data, and can potentially be used in the clinic to facilitate real-time stent analysis and visualization, aiding stent implantation.

Advances in Automated Assessment of Intracoronary Optical Coherence Tomography and Their Clinical Application

Intravascular optical coherence tomography (OCT) is capable of acquiring 3-dimensional (3D) data of coronary arteries allowing for the assessment of plaques, stents, thrombus, side branches, and other relevant structures in a 3D fashion. Given that state-of-the-art OCT systems acquire images at a very high frame rate (up to 200 frames per second), typically a very large number of images per pullback (ie, 500 or more) need to be analyzed. The manual assessment of stents, plaques, and other structures is time-consuming, cumbersome, and inefficient and thus not suitable for on-line analysis during percutaneous coronary intervention procedures.

Automatic detection of bioresorbable vascular scaffold struts in intravascular optical coherence tomography pullback runs

Bioresorbable vascular scaffolds (BVS) have gained significant interest in both the technical and clinical communities as a possible alternative to metallic stents. For accurate BVS analysis, intravascular optical coherence tomography (IVOCT) is currently the most suitable imaging technique due to its high resolution and the translucency of polymeric BVS struts for near infrared light. However, given the large number of struts in an IVOCT pullback run, quantitative analysis is only feasible when struts are detected automatically. In this paper, we present an automated method to detect and measure BVS struts based on their black cores in IVOCT images. Validated using 3 baseline and 3 follow-up data sets, the method detected 93.7% of 4691 BVS struts correctly with 1.8% false positives. In total, the Dice's coefficient for BVS strut areas was 0.84. It concludes that this method can detect BVS struts accurately and robustly for tissue coverage measurement, malapposition detection, strut distribution analysis or 3D scaffold reconstruction.

Fully automated side branch detection in intravascular optical coherence tomography pullback runs

Side branches in the atherosclerotic lesion region are important as they highly influence the treatment strategy selection and optimization. Moreover, they are reliable landmarks for image registration. By providing high resolution delineation of coronary morphology, intravascular optical coherence tomography (IVOCT) has been increasingly used for side branch analysis. This paper presents a fully automated method to detect side branches in IVOCT images, which relies on precise segmentation of the imaging catheter, the protective sheath, the guide wire and the lumen. 25 in-vivo data sets were used for validation. The intraclass correlation coefficient between the algorithmic results and manual delineations for the imaging catheter, the protective sheath and the lumen contour positions was 0.997, 0.949 and 0.974, respectively. All the guide wires were detected correctly and the Dice's coefficient of the shadow regions behind the guide wire was 0.97. 94.0% of 82 side branches were detected with 5.0% false positives and the Dice's coefficient of the side branch size was 0.85. In conclusion, the presented method has been demonstrated to be accurate and robust for side branch analysis.