Automatic segmentation of in-vivo intra-coronary optical coherence tomography images to assess stent strut apposition and coverage

Assessment of Effectiveness of the Algorithm for Automated Quantitative Analysis of Metallic Strut Tissue Short-Term Coverage with Intravascular Optical Coherence Tomography

Background: Due to its high resolution, optical coherence tomography (OCT) is the most suitable modality for neointimal coverage assessments. Evaluation of stent healing seems crucial to accurately define their safety profile since delayed healing is connected with stent thrombosis. This study aimed to present an algorithm for automated quantitative analysis of stent strut coverage at the early stages of vessel healing in intravascular OCT. Methods: A set of 592 OCT frames from 24 patients one month following drug-eluting stent implantation was used to assess the algorithm's effectiveness. Struts not covered on any side or covered but only on one side were categorized as uncovered. The algorithm consists of several key steps: preprocessing, vessel lumen segmentation, automatic strut detection, and measurement of neointimal thickness. Results: The proposed algorithm proved its efficiency in lumen and stent area estimation versus manual reference. It showed a high positive predictive value (PPV) (89.7%) and true positive rate (TPR) (91.4%) in detecting struts. A qualitative assessment for covered and uncovered struts was characterized by high TPR (99.1% and 80%, respectively, for uncovered and covered struts) and PPV (77.3% and 87%). Conclusions: The proposed algorithm demonstrated good agreement with manual measurements. Automating the stent coverage assessment might facilitate imaging analysis, which might be beneficial in experimental and clinical settings.

High-frequency optical coherence tomography for endovascular management of cerebral aneurysms

Endovascular management of intracranial aneurysms has become the mainstay of treatment in recent years; however, retreatment rates remain as high as 1 in 5. High-frequency optical coherence tomography (HF-OCT) is an emerging imaging modality for the assessment, treatment and follow-up of cerebral aneurysms. EMBASE and SCOPUS databases were searched for studies relating to the management of intracranial aneurysm with OCT. A combination of keywords were used including 'cerebral aneurysm', 'intracranial aneurysm', 'high-frequency optical coherence tomography', 'optical coherence tomography', and 'optical frequency domain imaging'. There were 23 papers included in this review. For the assessment of intracranial aneurysm, OCT was able to accurately assess aneurysm morphology as well as detailed analysis of arterial wall layers. During IA treatment, OCT was used to assess and troubleshoot stent placement to optimise successful isolation from the circulation. In the follow-up period, endothelial growth patterns were visualised by OCT imaging. OCT shows promise for the treatment of IAs at all stages of management. Due to the novel development of HF-OCT, there is limited longitudinal data in human studies. Further research in this area is required with a focus specifically on long-term treatment outcomes in humans.

Segmentation of anatomical layers and imaging artifacts in intravascular polarization sensitive optical coherence tomography using attending physician and boundary cardinality losses

Intravascular ultrasound and optical coherence tomography are widely available for assessing coronary stenoses and provide critical information to optimize percutaneous coronary intervention. Intravascular polarization-sensitive optical coherence tomography (PS-OCT) measures the polarization state of the light scattered by the vessel wall in addition to conventional cross-sectional images of subsurface microstructure. This affords reconstruction of tissue polarization properties and reveals improved contrast between the layers of the vessel wall along with insight into collagen and smooth muscle content. Here, we propose a convolutional neural network model, optimized using two new loss terms (Boundary Cardinality and Attending Physician), that takes advantage of the additional polarization contrast and classifies the lumen, intima, and media layers in addition to guidewire and plaque shadows. Our model segments the media boundaries through fibrotic plaques and continues to estimate the outer media boundary behind shadows of lipid-rich plaques. We demonstrate that our multi-class classification model outperforms existing methods that exclusively use conventional OCT data, predominantly segment the lumen, and consider subsurface layers at most in regions of minimal disease. Segmentation of all anatomical layers throughout diseased vessels may facilitate stent sizing and will enable automated characterization of plaque polarization properties for investigation of the natural history and significance of coronary atheromas.

Automatic lumen and anatomical layers segmentation in IVOCT images using meta learning

Automated analysis of the vessel structure in intravascular optical coherence tomography (IVOCT) images is critical to assess the health status of vessels and monitor coronary artery disease progression. However, deep learning-based methods usually require well-annotated large datasets, which are difficult to obtain in the field of medical image analysis. Hence, an automatic layers segmentation method based on meta-learning was proposed, which can simultaneously extract the surfaces of the lumen, intima, media, and adventitia using a handful of annotated samples. Specifically, we leverage a bi-level gradient strategy to train a meta-learner for capturing the shared meta-knowledge among different anatomical layers and quickly adapting to unknown anatomical layers. Then, a Claw-type network and a contrast consistency loss were designed to better learn the meta-knowledge according to the characteristic of annotation of the lumen and anatomical layers. Experimental results on the two cardiovascular IVOCT datasets show that the proposed method achieved state-of-art performance.

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10-20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional (3D) simulations of patients' arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarizes advances in automated segmentation techniques from the past five years (2016-2021) with a focus on their application to the 3D reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; artery layers; plaque characteristics and subtypes; and stents. Areas for future innovation are also discussed as well as their potential for future translation.

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.

Stent detection with very thick tissue coverage in intravascular OCT

Coronary stenting 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.

Automatic lumen segmentation using uniqueness of vascular connected region for intravascular optical coherence tomography

We present an automatic lumen segmentation method using uniqueness of connected region for intravascular optical coherence tomography (IVOCT), which can effectively remove the effect on lumen segmentation caused by blood artifacts. Utilizing the uniqueness of vascular wall on A-lines, we detect the A-lines shared by multiple connected regions, identify connected regions generated by blood artifacts using traversal comparison of connected regions' location, shared ratio and area ratio and then remove all artifacts. We compare these three methods by 216 challenging images with severe blood artifacts selected from clinical 1076 IVOCT images. The metrics of the proposed method are evaluated including Dice index, Jaccard index and accuracy of 94.57%, 90.12%, 98.02%. Compared with automatic lumen segmentation based on the previous morphological feature method and widely used dynamic programming method, the metrics of the proposed method are significantly enhanced, especially in challenging images with severe blood artifacts.

Shape prior generation and geodesic active contour interactive iterating algorithm (SPACIAL): fully automatic segmentation for 3D lumen in intravascular optical coherence tomography images

PURPOSE

Fully automatic lumen segmentation in intravascular optical coherence tomography (OCT) images can assist physicians in quickly estimating the health status of vessels. However, OCT images are usually degraded by residual blood, catheter walls, guide wire artifacts, etc., which significantly reduce the quality of segmentation. To achieve accurate lumen segmentation in low-quality images, we propose a novel segmentation algorithm named SPACIAL: Shape Prior generation and geodesic Active Contour Interactive iterAting aLgorithm, which is guided by an adaptively generated shape prior.

METHODS

In this framework, the active contour evolves under the guidance of shape prior, while the shape prior is automatically and adaptively generated based on the active contour. The active contour and the shape prior interactively iterate each other, which can generate the adaptive shape prior and consequently lead to accurate segmentation results. In addition, a fast algorithm is introduced to accelerate the segmentation in 3D images.

RESULTS

The validity of the model is verified in 3240 images from 12 OCT pullbacks. The experimental results show satisfactory segmentation accuracy and time efficiency: the average Dice coefficient of SPACIAL is 93.6(2.4)%, and 5.7 times faster than that of the classical level set method.

CONCLUSION

The proposed SPACIAL can quickly and efficiently perform accurate lumen segmentation on low quality OCT images, which is of great importance to cardiovascular disease diagnosis . The SPACIAL method shows great potential in clinical applications.

A Deep Segmentation Network of Multi-Scale Feature Fusion Based on Attention Mechanism for IVOCT Lumen Contour

Recently, coronary heart disease has attracted more and more attention, where segmentation and analysis for vascular lumen contour are helpful for treatment. And intravascular optical coherence tomography (IVOCT) images are used to display lumen shapes in clinic. Thus, an automatic segmentation method for IVOCT lumen contour is necessary to reduce the doctors' workload while ensuring diagnostic accuracy. In this paper, we proposed a deep residual segmentation network of multi-scale feature fusion based on attention mechanism (RSM-Network, Residual Squeezed Multi-Scale Network) to segment the lumen contour in IVOCT images. Firstly, three different data augmentation methods including mirror level turnover, rotation and vertical flip are considered to expand the training set. Then in the proposed RSM-Network, U-Net is contained as the main body, considering its characteristic of accepting input images with any sizes. Meanwhile, the combination of residual network and attention mechanism is applied to improve the ability of global feature extraction and solve the vanishing gradient problem. Moreover, the pyramid feature extraction structure is introduced to enhance the learning ability for multi-scale features. Finally, in order to increase the matching degree between the actual output and expected output, the cross entropy loss function is also used. A series of metrics are presented to evaluate the performance of our proposed network and the experimental results demonstrate that the proposed RSM-Network can learn the contour details better, contributing to strong robustness and accuracy for IVOCT lumen contour segmentation.

A neurovascular high-frequency optical coherence tomography system enables in situ cerebrovascular volumetric microscopy

Intravascular imaging has emerged as a valuable tool for the treatment of coronary and peripheral artery disease; however, no solution is available for safe and reliable use in the tortuous vascular anatomy of the brain. Endovascular treatment of stroke is delivered under image guidance with insufficient resolution to adequately assess underlying arterial pathology and therapeutic devices. High-resolution imaging, enabling surgeons to visualize cerebral arteries' microstructure and micron-level features of neurovascular devices, would have a profound impact in the research, diagnosis, and treatment of cerebrovascular diseases. Here, we present a neurovascular high-frequency optical coherence tomography (HF-OCT) system, including an imaging console and an endoscopic probe designed to rapidly acquire volumetric microscopy data at a resolution approaching 10 microns in tortuous cerebrovascular anatomies. Using a combination of in vitro, ex vivo, and in vivo models, the feasibility of HF-OCT for cerebrovascular imaging was demonstrated.

Automatic stent reconstruction in optical coherence tomography based on a deep convolutional model

Intravascular optical coherence tomography (IVOCT) can accurately assess stent apposition and expansion, thus enabling the optimisation of a stenting procedure to minimize the risk of device failure. This paper presents a deep convolutional based model for automatic detection and segmentation of stent struts. The input of pseudo-3D images aggregated the information from adjacent frames to refine the probability of strut detection. In addition, multi-scale shortcut connections were implemented to minimize the loss of spatial resolution and refine the segmentation of strut contours. After training, the model was independently tested in 21,363 cross-sectional images from 170 IVOCT image pullbacks. The proposed model obtained excellent segmentation (0.907 Dice and 0.838 Jaccard) and detection metrics (0.943 precision, 0.940 recall and 0.936 F1-score), significantly better than conventional features-based algorithms. This performance was robust and homogenous among IVOCT pullbacks with different sources of acquisition (clinical centres, imaging operators, type of stent, time of acquisition and challenging scenarios). In addition, excellent agreement between the model and a commercialized software was observed in the quantification of clinically relevant parameters. In conclusion, the deep-convolutional model can accurately detect stent struts in IVOCT images, thus enabling the fully-automatic quantification of stent parameters in an extremely short time. It might facilitate the application of quantitative IVOCT analysis in real-world clinical scenarios.

Application and Evaluation of Highly Automated Software for Comprehensive Stent Analysis in Intravascular Optical Coherence Tomography

Intravascular optical coherence tomography (IVOCT) is used to assess stent tissue coverage and malapposition in stent evaluation trials. We developed the OCT Image Visualization and Analysis Toolkit for Stent (OCTivat-Stent), for highly automated analysis of IVOCT pullbacks. Algorithms automatically detected the guidewire, lumen boundary, and stent struts; determined the presence of tissue coverage for each strut; and estimated the stent contour for comparison of stent and lumen area. Strut-level tissue thickness, tissue coverage area, and malapposition area were automatically quantified. The software was used to analyze 292 stent pullbacks. The concordance-correlation-coefficients of automatically measured stent and lumen areas and independent manual measurements were 0.97 and 0.99, respectively. Eleven percent of struts were missed by the software and some artifacts were miscalled as struts giving 1% false-positive strut detection. Eighty-two percent of uncovered struts and 99% of covered struts were labeled correctly, as compared to manual analysis. Using the highly automated software, analysis was harmonized, leading to a reduction of inter-observer variability by 30%. With software assistance, analysis time for a full stent analysis was reduced to less than 30 minutes. Application of this software to stent evaluation trials should enable faster, more reliable analysis with improved statistical power for comparing designs.

Deep principal dimension encoding for the classification of early neoplasia in Barrett's Esophagus with volumetric laser endomicroscopy

Barrett cancer is a treatable disease when detected at an early stage. However, current screening protocols are often not effective at finding the disease early. Volumetric Laser Endomicroscopy (VLE) is a promising new imaging tool for finding dysplasia in Barrett's esophagus (BE) at an early stage, by acquiring cross-sectional images of the microscopic structure of BE up to 3-mm deep. However, interpretation of VLE scans is difficult for medical doctors due to both the size and subtlety of the gray-scale data. Therefore, algorithms that can accurately find cancerous regions are very valuable for the interpretation of VLE data. In this study, we propose a fully-automatic multi-step Computer-Aided Detection (CAD) algorithm that optimally leverages the effectiveness of deep learning strategies by encoding the principal dimension in VLE data. Additionally, we show that combining the encoded dimensions with conventional machine learning techniques further improves results while maintaining interpretability. Furthermore, we train and validate our algorithm on a new histopathologically validated set of in-vivo VLE snapshots. Additionally, an independent test set is used to assess the performance of the model. Finally, we compare the performance of our algorithm against previous state-of-the-art systems. With the encoded principal dimension, we obtain an Area Under the Curve (AUC) and F₁ score of 0.93 and 87.4% on the test set respectively. We show this is a significant improvement compared to the state-of-the-art of 0.89 and 83.1%, respectively, thereby demonstrating the effectiveness of our approach.

A novel automated lumen segmentation and classification algorithm for detection of irregular protrusion after stents deployment

BACKGROUND

Clinically, irregular protrusions and blockages after stent deployment can lead to significant adverse outcomes such as thrombotic reocclusion or restenosis. In this study, we propose a novel fully automated method for irregular lumen segmentation and normal/abnormal lumen classification.

METHODS

The proposed method consists of a lumen segmentation, feature extraction, and lumen classification. In total, 92 features were extracted to classify normal/abnormal lumen. The lumen classification method is a combination of supervised learning algorithm and feature selection that is a partition-membership filter method.

RESULTS

As the results, our proposed lumen segmentation method obtained the average of dice similarity coefficient (DSC) and the accuracy of proposed features and the random forest (RF) for normal/abnormal lumen classification as 97.6% and 98.2%, respectively.

CONCLUSIONS

Therefore, we can lead to better understanding of the overall vascular status and help to determine cardiovascular diagnosis.

Automated accurate lumen segmentation using L-mode interpolation for three-dimensional intravascular optical coherence tomography

Intravascular optical coherence tomography (IVOCT) lumen-based computational flow dynamics (CFD) enables physiologic evaluations such as of the fractional flow reserve (FFR) and wall sheer stress. In this study, we developed an accurate, time-efficient method for extracting lumen contours of the coronary artery. The contours of cross-sectional images containing wide intimal discontinuities due to guide wire shadowing and large bifurcations were delineated by utilizing the natural longitudinal lumen continuity of the arteries. Our algorithm was applied to 5931 pre-intervention OCT images acquired from 40 patients. For a quantitative comparison, the images were also processed through manual segmentation (the reference standard) and automated ones utilizing cross-sectional and longitudinal continuities. The results showed that the proposed algorithm outperforms other schemes, exhibiting a strong correlation (R = 0.988) and overlapping and non-overlapping area ratios of 0.931 and 0.101, respectively. To examine the accuracy of the OCT-derived FFR calculated using the proposed scheme, a CFD simulation of a three-dimensional coronary geometry was performed. The strong correlation with a manual lumen-derived FFR (R = 0.978) further demonstrated the reliability and accuracy of our algorithm with potential applications in clinical settings.

Parallel deep neural networks for endoscopic OCT image segmentation

We report parallel-trained deep neural networks for automated endoscopic OCT image segmentation feasible even with a limited training data set. These U-Net-based deep neural networks were trained using a modified dice loss function and manual segmentations of ultrahigh-resolution cross-sectional images collected by an 800 nm OCT endoscopic system. The method was tested on in vivo guinea pig esophagus images. Results showed its robust layer segmentation capability with a boundary error of 1.4 µm insensitive to lay topology disorders. To further illustrate its clinical potential, the method was applied to differentiating in vivo OCT esophagus images from an eosinophilic esophagitis (EOE) model and its control group, and the results clearly demonstrated quantitative changes in the top esophageal layers' thickness in the EOE model.

Establishment of an Automated Algorithm Utilizing Optical Coherence Tomography and Micro-Computed Tomography Imaging to Reconstruct the 3-D Deformed Stent Geometry

Percutaneous coronary intervention (PCI) is the prevalent treatment for coronary artery disease, with hundreds of thousands of stents implanted annually. Computational studies have demonstrated the role of biomechanics in the failure of vascular stents, but clinical studies is this area are limited by a lack of understanding of the deployed stent geometry, which is required to accurately model and predict the stent-induced in vivo biomechanical environment. Herein, we present an automated method to reconstruct the 3-D deployed stent configuration through the fusion of optical coherence tomography (OCT) and micro-computed tomography ( μ CT) imaging data. In an experimental setup, OCT and μ CT data were collected in stents deployed in arterial phantoms ( n=4 ). A constrained iterative deformation process directed by diffeomorphic metric mapping was developed to deform μ CT data of a stent wireframe to the OCT-derived sparse point cloud of the deployed stent. Reconstructions of the deployed stents showed excellent agreement with the ground-truth configurations, with the distance between corresponding points on the reconstructed and ground-truth configurations of [Formula: see text]. Finally, reconstructions required <30 min of computational time. In conclusion, the developed and validated reconstruction algorithm provides a complete spatially resolved reconstruction of a deployed vascular stent from commercially available imaging modalities and has the potential, with further development, to provide more accurate computational models to evaluate the in vivo post-stent mechanical environment, as well as clinical visualization of the 3-D stent geometry immediately following PCI.

Optimized Computer-Aided Segmentation and Three-Dimensional Reconstruction Using Intracoronary Optical Coherence Tomography

We present a novel and time-efficient method for intracoronary lumen detection, which produces three-dimensional (3-D) coronary arteries using optical coherence tomographic (OCT) images. OCT images are acquired for multiple patients and longitudinal cross-section (LOCS) images are reconstructed using different acquisition angles. The lumen contours for each LOCS image are extracted and translated to 2-D cross-sectional images. Using two angiographic projections, the centerline of the coronary vessel is reconstructed in 3-D, and the detected 2-D contours are transformed to 3-D and placed perpendicular to the centerline. To validate the proposed method, 613 manual annotations from medical experts were used as gold standard. The 2-D detected contours were compared with the annotated contours, and the 3-D reconstructed models produced using the detected contours were compared to the models produced by the annotated contours. Wall shear stress (WSS), as dominant hemodynamics factor, was calculated using computational fluid dynamics and 844 consecutive 2-mm segments of the 3-D models were extracted and compared with each other. High Pearson's correlation coefficients were obtained for the lumen area (r = 0.98) and local WSS (r = 0.97) measurements, while no significant bias with good limits of agreement was shown in the Bland-Altman analysis. The overlapping and nonoverlapping areas ratio between experts' annotations and presented method was 0.92 and 0.14, respectively. The proposed computer-aided lumen extraction and 3-D vessel reconstruction method is fast, accurate, and likely to assist in a number of research and clinical applications.

Position Paper Computational Cardiology

Computational cardiology is the scientific field devoted to the development of methodologies that enhance our mechanistic understanding, diagnosis and treatment of cardiovascular disease. In this regard, the field embraces the extraordinary pace of discovery in imaging, computational modeling, and cardiovascular informatics at the intersection of atherogenesis and vascular biology. This paper highlights existing methods, practices, and computational models and proposes new strategies to support a multidisciplinary effort in this space. We focus on the means by that to leverage and coalesce these multiple disciplines to advance translational science and computational cardiology. Analyzing the scientific trends and understanding the current needs we present our perspective for the future of cardiovascular treatment.

Automatic analysis of bioresorbable vascular scaffolds in intravascular optical coherence tomography images

The bioresorbable vascular scaffold (BVS) is a new generation of bioresorbable scaffold (BRS) for the treatment of coronary artery disease. A potential challenge of BVS is malapposition, which may possibly lead to late stent thrombosis. It is therefore important to conduct malapposition analysis right after stenting. Since an intravascular optical coherence tomography (IVOCT) image sequence contains thousands of BVS struts, manual analysis is labor intensive and time consuming. Computer-based automatic analysis is an alternative, but faces some difficulties due to the interference of blood artifacts and the uncertainty of the struts number, position and size. In this paper, we propose a novel framework for a struts malapposition analysis that breaks down the problem into two steps. Firstly, struts are detected by a cascade classifier trained by AdaBoost and a region of interest (ROI) is determined for each strut to completely contain it. Then, strut boundaries are segmented within ROIs through dynamic programming. Based on the segmentation result, malapposition analysis is conducted automatically. Tested on 7 pullbacks labeled by an expert, our method correctly detected 91.5% of 5821 BVS struts with 12.1% false positives. The average segmentation Dice coefficient for correctly detected struts was 0.81. The time consumption for a pullback is 15 sec on average. We conclude that our method is accurate and efficient for BVS strut detection and segmentation, and enables automatic BVS malapposition analysis in IVOCT images.

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

Polymeric endovascular implants are the next step in minimally invasive vascular interventions. As an alternative to traditional metallic drug-eluting stents, these often-erodible scaffolds present opportunities and challenges for patients and clinicians. Theoretically, as they resorb and are absorbed over time, they obviate the long-term complications of permanent implants, but in the short-term visualization and therefore positioning is problematic. Polymeric scaffolds can only be fully imaged using optical coherence tomography (OCT) imaging-they are relatively invisible via angiography-and segmentation of polymeric struts in OCT images is performed manually, a laborious and intractable procedure for large datasets. Traditional lumen detection methods using implant struts as boundary limits fail in images with polymeric implants. Therefore, it is necessary to develop an automated method to detect polymeric struts and luminal borders in OCT images; we present such a fully automated algorithm. Accuracy was validated using expert annotations on 1140 OCT images with a positive predictive value of 0.93 for strut detection and an R2 correlation coefficient of 0.94 between detected and expert-annotated lumen areas. The proposed algorithm allows for rapid, accurate, and automated detection of polymeric struts and the luminal border in OCT images.

Patient-Specific Modeling of Stented Coronary Arteries Reconstructed from Optical Coherence Tomography: Towards a Widespread Clinical Use of Fluid Dynamics Analyses

The recent widespread application of optical coherence tomography (OCT) in interventional cardiology has improved patient-specific modeling of stented coronary arteries for the investigation of local hemodynamics. In this review, the workflow for the creation of fluid dynamics models of stented coronary arteries from OCT images is presented. The algorithms for lumen contours and stent strut detection from OCT as well as the reconstruction methods of stented geometries are discussed. Furthermore, the state of the art of studies that investigate the hemodynamics of OCT-based stented coronary artery geometries is reported. Although those studies analyzed few patient-specific cases, the application of the current reconstruction methods of stented geometries to large populations is possible. However, the improvement of these methods and the reduction of the time needed for the entire modeling process are crucial for a widespread clinical use of the OCT-based models and future in silico clinical trials.

Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography

Intravascular optical coherence tomography (OCT) is an optical imaging modality commonly used in the assessment of coronary artery diseases during percutaneous coronary intervention. Manual segmentation to assess luminal stenosis from OCT pullback scans is challenging and time consuming. We propose a linear-regression convolutional neural network to automatically perform vessel lumen segmentation, parameterized in terms of radial distances from the catheter centroid in polar space. Benchmarked against gold-standard manual segmentation, our proposed algorithm achieves average locational accuracy of the vessel wall of 22 microns, and 0.985 and 0.970 in Dice coefficient and Jaccard similarity index, respectively. The average absolute error of luminal area estimation is 1.38%. The processing rate is 40.6 ms per image, suggesting the potential to be incorporated into a clinical workflow and to provide quantitative assessment of vessel lumen in an intraoperative time frame.

ARCOCT: Automatic detection of lumen border in intravascular OCT images

BACKGROUND AND OBJECTIVE

Intravascular optical coherence tomography (OCT) is an invaluable tool for the detection of pathological features on the arterial wall and the investigation of post-stenting complications. Computational lumen border detection in OCT images is highly advantageous, since it may support rapid morphometric analysis. However, automatic detection is very challenging, since OCT images typically include various artifacts that impact image clarity, including features such as side branches and intraluminal blood presence. This paper presents ARCOCT, a segmentation method for fully-automatic detection of lumen border in OCT images.

METHODS

ARCOCT relies on multiple, consecutive processing steps, accounting for image preparation, contour extraction and refinement. In particular, for contour extraction ARCOCT employs the transformation of OCT images based on physical characteristics such as reflectivity and absorption of the tissue and, for contour refinement, local regression using weighted linear least squares and a 2nd degree polynomial model is employed to achieve artifact and small-branch correction as well as smoothness of the artery mesh. Our major focus was to achieve accurate contour delineation in the various types of OCT images, i.e., even in challenging cases with branches and artifacts.

RESULTS

ARCOCT has been assessed in a dataset of 1812 images (308 from stented and 1504 from native segments) obtained from 20 patients. ARCOCT was compared against ground-truth manual segmentation performed by experts on the basis of various geometric features (e.g. area, perimeter, radius, diameter, centroid, etc.) and closed contour matching indicators (the Dice index, the Hausdorff distance and the undirected average distance), using standard statistical analysis methods. The proposed method was proven very efficient and close to the ground-truth, exhibiting non statistically-significant differences for most of the examined metrics.

CONCLUSIONS

ARCOCT allows accurate and fully-automated lumen border detection in OCT images.

Contour segmentation of the intima, media, and adventitia layers in intracoronary OCT images: application to fully automatic detection of healthy wall regions

PURPOSE

Quantitative and automatic analysis of intracoronary optical coherence tomography images is useful and time-saving to assess cardiovascular risk in the clinical arena.

METHODS

First, the interfaces of the intima, media, and adventitia layers are segmented, by means of an original front propagation scheme, running in a 4D multi-parametric space, to simultaneously extract three non-crossing contours in the initial cross-sectional image. Second, information resulting from the tentative contours is exploited by a machine learning approach to identify healthy and diseased regions of the arterial wall. The framework is fully automatic.

RESULTS

The method was applied to 40 patients from two different medical centers. The framework was trained on 140 images and validated on 260 other images. For the contour segmentation method, the average segmentation errors were [Formula: see text] for the intima-media interface, [Formula: see text] for the media-adventitia interface, and [Formula: see text] for the adventitia-periadventitia interface. The classification method demonstrated a good accuracy, with a median Dice coefficient equal to 0.93 and an interquartile range of (0.78-0.98).

CONCLUSION

The proposed framework demonstrated promising offline performances and could potentially be translated into a reliable tool for various clinical applications, such as quantification of tissue layer thickness and global summarization of healthy regions in entire pullbacks.

A framework for computational fluid dynamic analyses of patient-specific stented coronary arteries from optical coherence tomography images

The clinical challenge of percutaneous coronary interventions (PCI) is highly dependent on the recognition of the coronary anatomy of each individual. The classic imaging modality used for PCI is angiography, but advanced imaging techniques that are routinely performed during PCI, like optical coherence tomography (OCT), may provide detailed knowledge of the pre-intervention vessel anatomy as well as the post-procedural assessment of the specific stent-to-vessel interactions. Computational fluid dynamics (CFD) is an emerging investigational tool in the setting of optimization of PCI results. In this study, an OCT-based reconstruction method was developed for the execution of CFD simulations of patient-specific coronary artery models which include the actual geometry of the implanted stent. The method was applied to a rigid phantom resembling a stented segment of the left anterior descending coronary artery. The segmentation algorithm was validated against manual segmentation. A strong correlation was found between automatic and manual segmentation of lumen in terms of area values. Similarity indices resulted >96% for the lumen segmentation and >77% for the stent strut segmentation. The 3D reconstruction achieved for the stented phantom was also assessed with the geometry provided by X-ray computed micro tomography scan, used as ground truth, and showed the incidence of distortion from catheter-based imaging techniques. The 3D reconstruction was successfully used to perform CFD analyses, demonstrating a great potential for patient-specific investigations. In conclusion, OCT may represent a reliable source for patient-specific CFD analyses which may be optimized using dedicated automatic segmentation algorithms.

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.

Automatic Lumen Segmentation in Intravascular Optical Coherence Tomography Images Using Level Set

Automatic lumen segmentation from intravascular optical coherence tomography (IVOCT) images is an important and fundamental work for diagnosis and treatment of coronary artery disease. However, it is a very challenging task due to irregular lumen caused by unstable plaque and bifurcation vessel, guide wire shadow, and blood artifacts. To address these problems, this paper presents a novel automatic level set based segmentation algorithm which is very competent for irregular lumen challenge. Before applying the level set model, a narrow image smooth filter is proposed to reduce the effect of artifacts and prevent the leakage of level set meanwhile. Moreover, a divide-and-conquer strategy is proposed to deal with the guide wire shadow. With our proposed method, the influence of irregular lumen, guide wire shadow, and blood artifacts can be appreciably reduced. Finally, the experimental results showed that the proposed method is robust and accurate by evaluating 880 images from 5 different patients and the average DSC value was 98.1% ± 1.1%.

Automated detection of vessel lumen and stent struts in intravascular optical coherence tomography to evaluate stent apposition and neointimal coverage

PURPOSE

Intravascular optical coherence tomography (IV-OCT) is a high-resolution imaging method used to visualize the microstructure of arterial walls in vivo. IV-OCT enables the clinician to clearly observe and accurately measure stent apposition and neointimal coverage of coronary stents, which are associated with side effects such as in-stent thrombosis. In this study, the authors present an algorithm for quantifying stent apposition and neointimal coverage by automatically detecting lumen contours and stent struts in IV-OCT images.

METHODS

The algorithm utilizes OCT intensity images and their first and second gradient images along the axial direction to detect lumen contours and stent strut candidates. These stent strut candidates are classified into true and false stent struts based on their features, using an artificial neural network with one hidden layer and ten nodes. After segmentation, either the protrusion distance (PD) or neointimal thickness (NT) for each strut is measured automatically. In randomly selected image sets covering a large variety of clinical scenarios, the results of the algorithm were compared to those of manual segmentation by IV-OCT readers.

RESULTS

Stent strut detection showed a 96.5% positive predictive value and a 92.9% true positive rate. In addition, case-by-case validation also showed comparable accuracy for most cases. High correlation coefficients (R > 0.99) were observed for PD and NT between the algorithmic and the manual results, showing little bias (0.20 and 0.46 μm, respectively) and a narrow range of limits of agreement (36 and 54 μm, respectively). In addition, the algorithm worked well in various clinical scenarios and even in cases with a low level of stent malapposition and neointimal coverage.

CONCLUSIONS

The presented automatic algorithm enables robust and fast detection of lumen contours and stent struts and provides quantitative measurements of PD and NT. In addition, the algorithm was validated using various clinical cases to demonstrate its reliability. Therefore, this technique can be effectively utilized for clinical trials on stent-related side effects, including in-stent thrombosis and in-stent restenosis.

Analysis and compensation for the effect of the catheter position on image intensities in intravascular optical coherence tomography

Intravascular optical coherence tomography (IVOCT) is an imaging technique that is used to analyze the underlying cause of cardiovascular disease. Because a catheter is used during imaging, the intensities can be affected by the catheter position. This work aims to analyze the effect of the catheter position on IVOCT image intensities and to propose a compensation method to minimize this effect in order to improve the visualization and the automatic analysis of IVOCT images. The effect of catheter position is modeled with respect to the distance between the catheter and the arterial wall (distance-dependent factor) and the incident angle onto the arterial wall (angle-dependent factor). A light transmission model incorporating both factors is introduced. On the basis of this model, the interaction effect of both factors is estimated with a hierarchical multivariant linear regression model. Statistical analysis shows that IVOCT intensities are significantly affected by both factors with p<0.001, as either aspect increases the intensity decreases. This effect differs for different pullbacks. The regression results were used to compensate for this effect. Experiments show that the proposed compensation method can improve the performance of the automatic bioresorbable vascular scaffold strut detection.

Complex bifurcation lesions: Randomized comparison of a fully bioresorbable modified t stenting strategy versus bifurcation reconstruction with a dedicated self-expanding stent in combination with bioresorbable scaffolds, an OCT study: Rationale and design of the COBRA II trial

OBJECTIVE

There is an ongoing controversy regarding the efficacy and safety of different percutaneous stenting techniques for coronary bifurcation lesions needing >1 stent. The promise of safe vessel restoration with bioresorbable scaffolds (BRS) may not be transferable to complex double BRS bifurcation techniques, and permanent metallic scaffolding of the bifurcation core may be needed. We identified modified-T stenting as the most promising fully bioresorbable 2-stent strategy in a preclinical setting. The objective of this study is to assess acute performance and compare long-term vessel healing with this strategy, versus an approach combining BRS with a dedicated metallic drug-eluting bifurcation stent.

STUDY DESIGN

In a single center, 60 consecutive patients with true and complex coronary bifurcation lesions will be randomly assigned to treatment with the dedicated self-expanding Axxess^™ biolimus-eluting bifurcation stent in the proximal main vessel and additional Absorb^™ everolimus-eluting BRS in the branches versus a modified T technique using Absorb^™ only. Angiography and optical coherence tomography (OCT) will be performed immediately after implantation and at 30 months, and clinical follow-up is foreseen up to 5 years after implantation. The primary endpoint is the change in minimal luminal area assessed with OCT from baseline to 30 months in pre-specified bifurcation segments.

CONCLUSION

To date the use of Absorb^™ BRS in complex coronary bifurcations has not been evaluated in a randomized clinical trial setting. The COBRA II study will examine the role and safety of a double BRS strategy in coronary bifurcations, alone or in combination with a metallic dedicated bifurcation device. © 2016 Wiley Periodicals, Inc.

Clinical Characterization of Coronary Atherosclerosis With Dual-Modality OCT and Near-Infrared Autofluorescence Imaging

OBJECTIVES

The authors present the clinical imaging of human coronary arteries in vivo using a multimodality optical coherence tomography (OCT) and near-infrared autofluorescence (NIRAF) intravascular imaging system and catheter.

BACKGROUND

Although intravascular OCT is capable of providing microstructural images of coronary atherosclerotic lesions, it is limited in its capability to ascertain the compositional/molecular features of plaque. A recent study in cadaver coronary plaque showed that endogenous NIRAF is elevated in necrotic core lesions. The combination of these 2 technologies in 1 device may therefore provide synergistic data to aid in the diagnosis of coronary pathology in vivo.

METHODS

We developed a dual-modality intravascular imaging system and 2.6-F catheter that can simultaneously acquire OCT and NIRAF data from the same location on the artery wall. This technology was used to obtain volumetric OCT-NIRAF images from 12 patients with coronary artery disease undergoing percutaneous coronary intervention. Images were acquired during a brief, nonocclusive 3- to 4-ml/s contrast purge at a speed of 100 frames/s and a pullback rate of 20 or 40 mm/s. OCT-NIRAF data were analyzed to determine the distribution of the NIRAF signal with respect to OCT-delineated plaque morphological features.

RESULTS

High-quality intracoronary OCT and NIRAF image data (>50-mm pullback length) were successfully acquired without complication in all patients (17 coronary arteries). The maximum NIRAF signal intensity of each plaque was compared with OCT-defined type, showing a statistically significant difference between plaque types (1-way analysis of variance, p < 0.0001). Interestingly, coronary arterial NIRAF intensity was elevated only focally in plaques with a high-risk morphological phenotype (p < 0.05), including OCT fibroatheroma, plaque rupture, and fibroatheroma associated with in-stent restenosis.

CONCLUSIONS

This OCT-NIRAF study demonstrates that dual-modality microstructural and fluorescence intracoronary imaging can be safely and effectively conducted in human patients. Our findings show that NIRAF is associated with a high-risk morphological plaque phenotype. The focal distribution of NIRAF in these lesions furthermore suggests that this endogenous imaging biomarker may provide complementary information to that obtained by structural imaging alone.

Constraining OCT with Knowledge of Device Design Enables High Accuracy Hemodynamic Assessment of Endovascular Implants

Background

Stacking cross-sectional intravascular images permits three-dimensional rendering of endovascular implants, yet introduces between-frame uncertainties that limit characterization of device placement and the hemodynamic microenvironment. In a porcine coronary stent model, we demonstrate enhanced OCT reconstruction with preservation of between-frame features through fusion with angiography and a priori knowledge of stent design.

Methods and Results

Strut positions were extracted from sequential OCT frames. Reconstruction with standard interpolation generated discontinuous stent structures. By computationally constraining interpolation to known stent skeletons fitted to 3D ‘clouds’ of OCT-Angio-derived struts, implant anatomy was resolved, accurately rendering features from implant diameter and curvature (n = 1 vessels, r² = 0.91, 0.90, respectively) to individual strut-wall configurations (average displacement error ~15 μm). This framework facilitated hemodynamic simulation (n = 1 vessel), showing the critical importance of accurate anatomic rendering in characterizing both quantitative and basic qualitative flow patterns. Discontinuities with standard approaches systematically introduced noise and bias, poorly capturing regional flow effects. In contrast, the enhanced method preserved multi-scale (local strut to regional stent) flow interactions, demonstrating the impact of regional contexts in defining the hemodynamic consequence of local deployment errors.

Conclusion

Fusion of planar angiography and knowledge of device design permits enhanced OCT image analysis of in situ tissue-device interactions. Given emerging interests in simulation-derived hemodynamic assessment as surrogate measures of biological risk, such fused modalities offer a new window into patient-specific implant environments.

Automated segmentation and characterization of esophageal wall in vivo by tethered capsule optical coherence tomography endomicroscopy

Optical coherence tomography (OCT) is an optical diagnostic modality that can acquire cross-sectional images of the microscopic structure of the esophagus, including Barrett's esophagus (BE) and associated dysplasia. We developed a swallowable tethered capsule OCT endomicroscopy (TCE) device that acquires high-resolution images of entire gastrointestinal (GI) tract luminal organs. This device has a potential to become a screening method that identifies patients with an abnormal esophagus that should be further referred for upper endoscopy. Currently, the characterization of the OCT-TCE esophageal wall data set is performed manually, which is time-consuming and inefficient. Additionally, since the capsule optics optimally focus light approximately 500 µm outside the capsule wall and the best quality images are obtained when the tissue is in full contact with the capsule, it is crucial to provide feedback for the operator about tissue contact during the imaging procedure. In this study, we developed a fully automated algorithm for the segmentation of in vivo OCT-TCE data sets and characterization of the esophageal wall. The algorithm provides a two-dimensional representation of both the contact map from the data collected in human clinical studies as well as a tissue map depicting areas of BE with or without dysplasia. Results suggest that these techniques can potentially improve the current TCE data acquisition procedure and provide an efficient characterization of the diseased esophageal wall.

Vascular Response to Experimental Stent Malapposition and Under-Expansion

Up to 80% of all endovascular stents have malapposed struts, and while some impose catastrophic events others are inconsequential. Thirteen stents were implanted in coronary arteries of seven healthy Yorkshire pigs, using specially-designed cuffed balloons inducing controlled stent malapposition and under-expansion. Optical coherence tomography (OCT) imaging confirmed that 25% of struts were malapposed (strut-wall distance <strut thickness) to variable extent (max. strut-wall distance malapposed group 0.51 ± 0.05 mm vs. apposed group 0.09 ± 0.05 mm, p = 2e-3). Imaging at follow-up revealed malapposition acutely resolved (<1% of struts remained malapposed at day 5), with strong correlation between lumen and the stent cross-sectional areas (slope = 0.86, p < 0.0001, R (2) = 0.94). OCT in three of the most significantly malapposed vessels at baseline showed high correlation of elastic lamina area and lumen area (R (2) = 0.96) suggesting all lumen loss was related to contraction of elastic lamina with negligible plaque/intimal hyperplasia growth. Simulation showed this vascular recoil could be partially explained by the non-uniform strain environment created from sub-optimal expansion of device and balloon, and the inability of stent support in the malapposed region to resist recoil. Malapposition as a result of stent under-expansion is resolved acutely in healthy normal arteries, suggesting existing animal models are limited in replicating clinically observed persistent stent malapposition.

Error propagation in the characterization of atheromatic plaque types based on imaging

Imaging systems transmit and acquire signals and are subject to errors including: error sources, signal variations or possible calibration errors. These errors are included in all imaging systems for atherosclerosis and are propagated to methodologies implemented for the segmentation and characterization of atherosclerotic plaque. In this paper, we present a study for the propagation of imaging errors and image segmentation errors in plaque characterization methods applied to 2D vascular images. More specifically, the maximum error that can be propagated to the plaque characterization results is estimated, assuming worst-case scenarios. The proposed error propagation methodology is validated using methods applied to real datasets, obtained from intravascular imaging (IVUS) and optical coherence tomography (OCT) for coronary arteries, and magnetic resonance imaging (MRI) for carotid arteries. The plaque characterization methods have recently been presented in the literature and are able to detect the vessel borders, and characterize the atherosclerotic plaque types. Although, these methods have been extensively validated using as gold standard expert annotations, by applying the proposed error propagation methodology a more realistic validation is performed taking into account the effect of the border detection algorithms error and the image formation error into the final results. The Pearson's coefficient of the detected plaques has changed significantly when the method was applied to IVUS and OCT, while there was not any variation when the method was applied to MRI data.

Grading of Regional Apposition after Flow-Diverter Treatment (GRAFT): a comparative evaluation of VasoCT and intravascular OCT

Background

Poor vessel wall apposition of flow diverter (FD) stents poses risks for stroke-related complications when treating intracranial aneurysms, necessitating long-term surveillance imaging. To facilitate quantitative evaluation of deployed devices, a novel algorithm is presented that generates intuitive two-dimensional representations of wall apposition from either high-resolution contrast-enhanced cone-beam CT (VasoCT) or intravascular optical coherence tomography (OCT) images.

Methods

VasoCT and OCT images were obtained after FD implant (n=8 aneurysms) in an experimental sidewall aneurysm model in canines. Surface models of the vessel wall and FD device were extracted, and the distance between them was presented on a two-dimensional flattened map. Maps and cross-sections at potential locations of malapposition detected on VasoCT-based maps were compared. The performance of OCT-based apposition detection was evaluated on manually labeled cross-sections using logistic regression against a thresholded (≥0.25 mm) apposition measure.

Results

VasoCT and OCT acquisitions yielded similar Grading of Regional Apposition after Flow-Diverter Treatment (GRAFT) apposition maps. GRAFT maps from VasoCT highlighted 16 potential locations of malapposition, of which two were found to represent malapposed device struts. Logistic regression showed that OCT could detect malapposition with a sensitivity of 98% and a specificity of 81%.

Conclusions

GRAFT delivered quantitative and visually convenient representations of potential FD malapposition and occasional acute thrombus formation. A powerful combination for future neuroendovascular applications is foreseen with the superior resolution delivered by intravascular OCT.

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.

Currently available methodologies for the processing of intravascular ultrasound and optical coherence tomography images

Optical coherence tomography and intravascular ultrasound are the most widely used methodologies in clinical practice as they provide high resolution cross-sectional images that allow comprehensive visualization of the lumen and plaque morphology. Several methods have been developed in recent years to process the output of these imaging modalities, which allow fast, reliable and reproducible detection of the luminal borders and characterization of plaque composition. These methods have proven useful in the study of the atherosclerotic process as they have facilitated analysis of a vast amount of data. This review presents currently available intravascular ultrasound and optical coherence tomography processing methodologies for segmenting and characterizing the plaque area, highlighting their advantages and disadvantages, and discusses the future trends in intravascular imaging.

Dual modality intravascular optical coherence tomography (OCT) and near-infrared fluorescence (NIRF) imaging: a fully automated algorithm for the distance-calibration of NIRF signal intensity for quantitative molecular imaging

Intravascular optical coherence tomography (IVOCT) is a well-established method for the high-resolution investigation of atherosclerosis in vivo. Intravascular near-infrared fluorescence (NIRF) imaging is a novel technique for the assessment of molecular processes associated with coronary artery disease. Integration of NIRF and IVOCT technology in a single catheter provides the capability to simultaneously obtain co-localized anatomical and molecular information from the artery wall. Since NIRF signal intensity attenuates as a function of imaging catheter distance to the vessel wall, the generation of quantitative NIRF data requires an accurate measurement of the vessel wall in IVOCT images. Given that dual modality, intravascular OCT-NIRF systems acquire data at a very high frame-rate (>100 frames/s), a high number of images per pullback need to be analyzed, making manual processing of OCT-NIRF data extremely time consuming. To overcome this limitation, we developed an algorithm for the automatic distance-correction of dual-modality OCT-NIRF images. We validated this method by comparing automatic to manual segmentation results in 180 in vivo images from six New Zealand White rabbit atherosclerotic after indocyanine-green injection. A high Dice similarity coefficient was found (0.97 ± 0.03) together with an average individual A-line error of 22 µm (i.e., approximately twice the axial resolution of IVOCT) and a processing time of 44 ms per image. In a similar manner, the algorithm was validated using 120 IVOCT clinical images from eight different in vivo pullbacks in human coronary arteries. The results suggest that the proposed algorithm enables fully automatic visualization of dual modality OCT-NIRF pullbacks, and provides an accurate and efficient calibration of NIRF data for quantification of the molecular agent in the atherosclerotic vessel wall.