This document may be broadly used as a standard reference regarding the current state of the IVOCT imaging modality, intended for researchers and clinicians who use IVOCT and analyze IVOCT data.

The implantation of intracoronary stents is currently the standard approach for the treatment of coronary atherosclerotic disease. The widespread adoption of this technology has boosted an intensive research activity in this domain, with continuous improvements in the design of these devices, aiming at reducing problems of restenosis (re-narrowing of the stented segment) and thrombosis (sudden occlusion due to thrombus formation). Recently, a new, light-based intracoronary imaging modality, optical coherence tomography (OCT), was developed and introduced into clinical practice. Due to its very high axial resolution (10-15 μm), it allows for in vivo evaluation of both stent strut apposition and neointima coverage (a marker of healing of the treated segment). As such, it provides valuable information on proper stent deployment, on the behaviour of different stent types in-vivo and on the effect of new types of stents (e.g. drug-eluting stents) on vessel wall healing. However, the major drawback of the current OCT methodology is that analysis of these images requires a tremendous amount of-currently manual-post-processing. In this manuscript, an algorithm is presented that allows for fully automated analysis of stent strut apposition and coverage in coronary arteries. The vessel lumen and stent struts are automatically detected and segmented through analysis of the intensity profiles of the A-lines. From these data, apposition and coverage can then be measured automatically. The algorithm was validated using manual assessments by two experienced operators as a reference. High Pearson's correlation coefficients were found (R = 0.96-0.97) between the automated and manual measurements while Bland-Altman analysis showed no significant bias with good limits of agreement. As such, it was shown that the presented algorithm provides a robust and fast tool to automatically estimate apposition and coverage of stent struts in in-vivo OCT pullbacks. This will be important for the integration of this technology in clinical routine and for the analysis of datasets of larger clinical trials.

OBJECTIVES-We present the first clinical imaging of human coronary arteries in vivo using a multimodality OCT and near-infrared autofluorescence (NIRAF) intravascular imaging system and catheter.BACKGROUND-While intravascular OCT is capable of providing microstructural images of coronary atherosclerotic lesions, it is limited in its capability to ascertain compositional/molecular features of plaque, including the definitive presence of a necrotic core. A recent study in cadaver coronary plaque has shown that endogenous NIRAF is elevated in necrotic core lesions. The combination of these two technologies in one device may therefore provide synergistic data to aid in the diagnosis of coronary pathology in vivo.

Intravascular optical coherence tomography (IVOCT) is rapidly becoming the method of choice for the in vivo investigation of coronary artery disease. While IVOCT visualizes atherosclerotic plaques with a resolution <20µm, image analysis in terms of tissue composition is currently performed by a time-consuming manual procedure based on the qualitative interpretation of image features. We illustrate an algorithm for the automated and systematic characterization of IVOCT atherosclerotic tissue. The proposed method consists in a supervised classification of image pixels according to textural features combined with the estimated value of the optical attenuation coefficient. IVOCT images of 64 plaques, from 49 in vivo IVOCT data sets, constituted the algorithm’s training and testing data sets. Validation was obtained by comparing automated analysis results to the manual assessment of atherosclerotic plaques. An overall pixel-wise accuracy of 81.5% with a classification feasibility of 76.5% and per-class accuracy of 89.5%, 72.1% and 79.5% for fibrotic, calcified and lipid-rich tissue respectively, was found. Moreover, measured optical properties were in agreement with previous results reported in literature. As such, an algorithm for automated tissue characterization was developed and validated using in vivo human data, suggesting that it can be applied to clinical IVOCT data. This might be an important step towards the integration of IVOCT in cardiovascular research and routine clinical practice.