Intracoronary Optical Coherence Tomography Imaging Research Articles

Proteomics associated with coronary high-risk plaques by optical coherence tomography.

Biomarkers are widely used for the diagnosis and monitoring of cardiovascular disease. However, markers for coronary high-risk plaques have not been identified.The aim of this study was to identify proteins specific to coronary high-risk plaques.Fifty-one patients (71.2 ± 11.1 years, male: 66.7%) who underwent intracoronary optical coherence tomography imaging and provided blood specimens for proteomic analysis were prospectively enrolled. A total of 1470 plasma proteins were analyzed per patient using the Olink® Explore 1536 Reagent Kit.In patients with thin-cap fibroatheroma, the protein expression of Calretinin (CALB2), Corticoliberin (CRH) and Alkaline phosphatase, placental type (ALPP) were significantly increased, while the expression of Neuroplastin (NPTN), Folate receptor gamma (FOLR3) and Serpin A12 (SERPINA12) were significantly decreased. In patients with macrophage infiltration, the protein expressions of Fatty acid-binding protein, intestinal (FABP2), and Fibroblast growth factor 21 (FGF21) were significantly decreased. In patients with lipid-rich plaques, the protein expression of Interleukin-17C (IL17C) was significantly increased, while the expression of Fc receptor-like protein 3 (FCRL3) was significantly decreased.These proteins might be useful markers in identifying patients with coronary high-risk plaques. Clinical Trial Registration: https://www.umin.ac.jp/ctr/ , UMIN000041692.

Deep-learning based analysis of intracoronary optical coherence tomography images: detection and characterization of lipid plaques

Abstract Background Intracoronary optical coherence tomography (OCT) enables in vivo detection and characterization of atherosclerotic disease. However, manual assessment of OCT images is time-consuming and subject to intra- and interobserver variability. Therefore, automated and trustworthy methods for plaque assessment are warranted. Purpose To develop and validate an artificial intelligence based algorithm for detection and characterization of lipid plaques on OCT images. Methods From the prospective, observational PECTUS-obs study, we studied a representative subset of OCT pullbacks performed in fractional flow reserve-negative non-culprit lesions in patients presenting with myocardial infarction. Manual segmentation of cross-sectional frames (e.g. lumen, tunica intima including the fibrous cap, tunica media and lipid pools) was performed by trained experts. Pullbacks were randomly divided into a training and test set. For automated segmentation and analysis, a two-dimensional no-new U-shaped neural network (nnUNet) was constructed with 5-fold cross validation. To test the diagnostic performance of the nnUnet for detection of lipid plaques, sensitivity, specificity and Cohen’s kappa were calculated. As for plaque characterization, the lipid arc and minimal fibrous cap thickness were measured manually in the test set on each frame containing a lipid plaque. Values of lipid arc and minimal fibrous cap thickness obtained following automated assessment were compared to manual analysis using the intraclass correlation coefficient (ICC) for absolute agreement. Results In total, 1215 frames were used for training and 162 frames for testing of the nnUNet. Lipid plaques were present in 77 frames (47.5%) in the test set. Substantial agreement (κ=0.79) for detection of a lipid plaque was achieved with a sensitivity and specificity of 96.1% and 83.5%, respectively. As for plaque characterization, we found good reliability for lipid arc assessment (ICC 0.88, mean difference 6±32º) and moderate reliability for assessment of the minimal fibrous cap thickness (ICC 0.54, mean difference 23±140µm). Conclusion We developed a deep learning algorithm for automated analysis of intracoronary OCT images, that was able to accurately detect lipid plaques. Moreover, the algorithm showed promising results in terms of plaque characterization. With further refinements, the developed algorithm has the potential to reduce the time required for OCT interpretation, ultimately enabling more efficient, real-time use of this imaging modality in clinical practice.

Abstract 11731: Efficacy of a New Generation Fast-Pullback, High-Frequency Intracoronary Optical Coherence Tomography Imaging on the Reduction of Contrast Medium in Clinical Practice

Introduction: Intracoronary optical coherence tomography (OCT) is an imaging modality that uses near-infrared light to generate high-resolution images, and now plays an important role in research and clinical practice as it provides insights into vascular biology and diagnostic guidance for percutaneous intervention (PCI). An existing concern is that OCT imaging could increase the total amount of contrast media administered during the procedure, resulting in adverse outcomes such as impairment of renal function or worsening of heart failure in some patients. Aims: We sought to investigate whether a novel, fast-pullback, high-frequency OCT (HF-OCT) enables acquisitions with a reduced amount of contrast agents while retaining the same qualitative and quantitative lesion assessment to conventional OCT. Methods: This study is a single-center, prospective, observational study including 10 patients with stable coronary artery disease. A total of 28 individual coronary arteries were assessed by both fast-pullback HF-OCT and by conventional OCT. The fast-pullback, HF-OCT system consists of a 1.8-Fr Vis-Rx® Micro-Imaging Catheter and an HF-OCT Imaging console (Gentuity LLC, Sudbury, MA). Results: The contrast volume used in each OCT run for the HF-OCT system was significantly lower than for the conventional OCT system (5.0±0.0 vs. 7.8±0.7ml, P<0.001). No significant difference was found in the median value of the clear image length between the 2 OCT systems (74 mm [IQR; 63, 81], 74 mm [IQR; 71, 75], P=0.89). Fast-pullback HF-OCT showed comparable measurements to conventional OCT, including minimum lumen area (3.27±1.53 vs. 3.21±1.53 mm2, P=0.27), proximal reference area (7.03±2.28 vs. 7.03±2.34 mm2, P=0.96), and distal reference area (5.93±1.96 vs. 6.03±2.02 mm2, P=0.23). Qualitative OCT findings were comparable between the fast-pullback HF-OCT runs and conventional OCT with respect to identifying lipid-rich plaques, calcifications, layered plaques, macrophages, and cholesterol crystals. Conclusions: With the fast pullback function of a novel HF-OCT imaging system, we acquired OCT images using a significantly lower amount of contrast volume while retaining a comparable qualitative and quantitative lesion assessment to conventional OCT.

GANs-Based Intracoronary Optical Coherence Tomography Image Augmentation for Improved Plaques Characterization Using Deep Neural Networks

Data augmentation using generative adversarial networks (GANs) is vital in the creation of new instances that include imaging modality tasks for improved deep learning classification. In this study, conditional generative adversarial networks (cGANs) were used on a dataset of OCT (Optical Coherence Tomography)-acquired images of coronary atrial plaques for synthetic data creation for the first time, and further validated using deep learning architecture. A new OCT images dataset of 51 patients marked by three professionals was created and programmed. We used cGANs to synthetically populate the coronary aerial plaques dataset by factors of 5×, 10×, 50× and 100× from a limited original dataset to enhance its volume and diversification. The loss functions for the generator and the discriminator were set up to generate perfect aliases. The augmented OCT dataset was then used in the training phase of the leading AlexNet architecture. We used cGANs to create synthetic images and envisaged the impact of the ratio of real data to synthetic data on classification accuracy. We illustrated through experiments that augmenting real images with synthetic images by a factor of 50× during training helped improve the test accuracy of the classification architecture for label prediction by 15.8%. Further, we performed training time assessments against a number of iterations to identify optimum time efficiency. Automated plaques detection was found to be in conformity with clinical results using our proposed class conditioning GAN architecture.

Efficacy of a new generation intracoronary optical coherence tomography imaging system with fast pullback.

We sought to investigate whether a novel, fast-pullback, high-frequency optical coherence tomography (HF-OCT) imaging system enables data acquisition with a reduced amount of contrast agents while retaining the same qualitative and quantitative lesion assessment to conventional OCT. The increased amount of administered contrast agents is a major concern when performing intracoronary OCT. The present study is a single-center, prospective, observational study including 10 patients with stable coronary artery disease. A total of 28 individual coronary arteries were assessed by both fast-pullback HF-OCT and by conventional OCT. The contrast volume used in each OCT run for the HF-OCT system was significantly lower than for the conventional OCT system (5.0 ± 0.0 mL vs. 7.8 ± 0.7 mL, respectively, with a mean difference of -2.84 [95% confidence interval [CI]: -3.10 to -2.58]). No significant difference was found in the median value of the clear image length between the two OCT systems (74 mm [interquartile range[IQR]; 63, 81], 74 mm [IQR; 71, 75], p = 0.89). Fast-pullback HF-OCT showed comparable measurements to conventional OCT, including minimum lumen area (3.27 ± 1.53 mm2 vs. 3.21 ± 1.53 mm2 , p = 0.27), proximal reference area (7.03 ± 2.28 mm2 vs. 7.03 ± 2.34 mm2 , p = 0.96), and distal reference area (5.93 ± 1.96 mm2 vs. 6.03 ± 2.02 mm2 , p = 0.23). Qualitative OCT findings were comparable between the fast-pullback HF-OCT runs and conventional OCT with respect to identifying lipid-rich plaques, calcifications, layered plaques, macrophages, and cholesterol crystals. With the fast pullback function of a novel HF-OCT imaging system, we acquired OCT images using a significantly lower amount of contrast volume while retaining a comparable qualitative and quantitative lesion assessment to conventional OCT.

Immersive educational curriculum on intracoronary optical coherence tomography image analysis among naïve readers

BackgroundOptical coherence tomography (OCT) is an intravascular imaging modality for analysing coronary vessels. Image interpretation remains an obstacle for novice readers due to technical artefacts and uncertainty in tissue characterization. Despite an expanding clinical and research role for OCT, few training efforts exist, and there is an absence of a national standardized educational curriculum. We sought to determine whether an interactive, feedback-based OCT curriculum improved image interpretation among naive readers.MethodsNaive OCT readers completed both a Standard curriculum, comprised of self-directed didactics and consensus statements, and an Augmented curriculum, which provided real-time digital feedback of feature identification and measurements. Modules were separated by a minimum one-week washout period. After each module, and blinded to the exam answers, subjects completed an identical expert-designed 413-item exam to assess technical knowledge and ability to identify and measure vessel features. Performances were compared using Exact Wilcoxon signed-rank tests.ResultsAmong the 7 included subjects were 3 medical students, 3 internal medicine residents, and 1 cardiovascular medicine fellow with no prior OCT experience. The technical knowledge score (maximum 13) was significantly higher with the Augmented compared with the Standard curriculum (median 11 vs. 7, p = 0.03). After undergoing the Augmented curriculum, all 7 subjects were able to identify features of plaque rupture (Standard curriculum: 5/7 subjects, p = 0.5) and macrophages (Standard curriculum: 6/7 subjects, p = 0.99), differentiate the components between red and white thrombus (Standard curriculum: 6/7 subjects, p = 0.99), and characterize lipid plaque by attenuation, signal, homogeneity, and borders (Standard curriculum: 5/7 subjects, p = 0.5). Performances on the remaining exam portions did not differ between curricula.ConclusionsThe need for standardized, effective training in OCT image interpretation is increasingly essential as the intravascular imaging modality becomes widely utilized among interventional cardiologists and trainees. A novel interactive OCT curriculum enhanced naive readers’ technical knowledge and may supplement traditional self-learning in refining analytic skills.

Atherosclerotic Coronary Plaque features in patients with Acute Coronary Syndrome and Chronic Obstructive Pulmonary Disease

Abstract Background Chronic obstructive pulmonary disease (COPD) is a common respiratory disorder characterized by airflow limitation, persistent respiratory symptoms and chronic lung inflammation. Previous studies reported a robust relationship between COPD and coronary artery disease (CAD). Local and systemic inflammation, known to play a role in atherosclerosis development, has been reported in patients with COPD, and has been proposed as one of the possible pathogenetic factors linking COPD and CAD. However, data on atherosclerotic coronary pattern and coronary inflammation in COPD patients are lacking. Purpose To study the characteristics of atherosclerotic coronary plaques and local inflammation by using optical coherence tomography (OCT) in COPD patients presenting with acute coronary syndromes (ACS). Methods ACS patients undergoing intracoronary OCT imaging of the culprit vessel were retrospectively identified. Coronary plaque characteristics and OCT-defined macrophage infiltration (MØI) were assessed by OCT. ACS patients were divided into 2 groups according to the presence or the absence of an established diagnosis of COPD, and coronary plaque features and MØI both at the culprit plaque site and along the culprit vessel were compared between the two groups. Results Among 146 ACS patients (mean age, 66,1±12,7 years, 109 males), 47 (32,2%) had COPD. Patients with COPD were older, were more frequently on therapy with an angiotensin receptor blocker, had lower hemoglobin, total cholesterol and triglycerides levels, and had higher serum creatinine levels. The prevalence of different mechanisms of ACS were similar between COPD and noCOPD patients (plaque rupture: 57,4% vs. 45,4%, plaque erosion: 23,4% vs. 32,3%, calcified plaque: 19,1% vs. 22,2%, respectively, overall p=0,381). OCT analysis of plaque microstructures showed that COPD patients had significantly higher prevalence of MØI (78,7% vs. 54,4%, p=0,005), thin cap fibroatheroma (TCFA) (48,9% vs. 22,2%, p=0,001), spotty calcium (68,1% vs. 26,3%, p<0,001) and calcifications (83,0% vs. 66,7%, p=0,040) at the culprit site than noCOPD patients. In the multivariate logistic regression analysis performed to adjust for clinical features, COPD was independently associated with MØI both at the culprit site (OR: 4,726, CI: 1,599; 13,971, p=0,005) and along the culprit vessel (OR: 2,193, CI: 1,110; 4,332, p=0,024). Similarly, COPD was independently associated with the presence of TCFA both at the culprit site (OR: 5,737, CI: 1,877; 17,540, p=0,002) and along the culprit vessel (OR: 2,796, CI: 1,408; 5,553, p=0,003). Conclusions In ACS patients undergoing OCT imaging of the culprit vessel, COPD was an independent predictor of local plaque inflammation and plaque vulnerability both at the culprit site and along the culprit vessel. Our results may suggest that a higher inflammatory milieu in COPD patients might enhance local coronary inflammation, promoting CAD development and plaque vulnerability. Funding Acknowledgement Type of funding sources: None.

Automated diagnosis of optical coherence tomography imaging on plaque vulnerability and its relation to clinical outcomes in coronary artery disease

This study sought to develop a deep learning-based diagnostic algorithm for plaque vulnerability by analyzing intravascular optical coherence tomography (OCT) images and to investigate the relation between AI-plaque vulnerability and clinical outcomes in patients with coronary artery disease (CAD). A total of 1791 study patients who underwent OCT examinations were recruited from a multicenter clinical database, and the OCT images were first labeled as either normal, a stable plaque, or a vulnerable plaque by expert cardiologists. A DenseNet-121-based deep learning algorithm for plaque characterization was developed by training with 44,947 prelabeled OCT images, and demonstrated excellent differentiation among normal, stable plaques, and vulnerable plaques. Patients who were diagnosed with vulnerable plaques by the algorithm had a significantly higher rate of both events from the OCT-observed segments and clinical events than the patients with normal and stable plaque (log-rank p < 0.001). On the multivariate logistic regression analyses, the OCT diagnosis of a vulnerable plaque by the algorithm was independently associated with both types of events (p = 0.047 and p < 0.001, respectively). The AI analysis of intracoronary OCT imaging can assist cardiologists in diagnosing plaque vulnerability and identifying CAD patients with a high probability of occurrence of future clinical events.

P2428Comparison of Newtonian and non-Newtonian rheology in calculation of endothelial shear stress

Abstract Background Although blood is a non-Newtonian fluid, most clinical computational fluid dynamic (CFD) studies assume blood to be a Newtonian fluid with constant viscosity. At higher blood flow rates in larger arteries, the two models should present similar results, and the Newtonian assumption can be considered acceptable. However, whether the Newtonian assumption is valid in patient-specific coronary arteries under pulsatile flow has not been evaluated. Purpose To compare CFD results using Newtonian and non-Newtonian models of blood in order to determine whether the Newtonian assumption can be considered valid in patient-specific coronary arteries. Methods Coronary arteries of 16 patients were reconstructed from fusion of angiography and intracoronary optical coherence tomography imaging. Pulsatile CFD simulations using Newtonian and non-Newtonian models were performed to calculate endothelial shear stress (ESS). The absolute and percent difference in time-averaged and instantaneous ESS values (calculated as non-Newtonian minus Newtonian) were compared on a point-to-point basis. The percent area of the vessel exposed to proatherogenic ESS values (considered &lt;1 Pa) in each model was also calculated. Results The Newtonian and non-Newtonian models produce similar qualitative distributions of ESS. However, quantitative comparison shows that compared to the Newtonian results, the non-Newtonian model estimates significantly higher time-averaged ESS (2.04±0.63Pa versus 1.59±0.54Pa, 95% CI 0.39–0.49, p&lt;0.001) throughout the cardiac cycle. This results in significantly greater estimate of area exposed to ESS &lt;1Pa in the Newtonian model (50.43±14.16% versus 37.20±13.57%, 95% CI 11.28–15.18, p&lt;0.001). Instantaneous ESS plotted through the cardiac cycle indicates the greatest divergence in ESS values occurs at the transition between end-systole and early diastole, at approximately 0.35 seconds (FIGURE). Conclusions Despite similar qualitative ESS distributions, Newtonian and non-Newtonian simulations provide significantly different quantitative ESS values. This suggests that in patient-specific simulations of coronary blood flow, the non-Newtonian model may increase accuracy of ESS measurements. We hypothesize that using a non-Newtonian model may improve the diagnostic accuracy of abnormal ESS to predict clinically significant progression of atherosclerosis, however further study is necessary.

A transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: Validation by serial intracoronary OCT imaging

BackgroundFibrous cap thickness (FCT), best measured by intravascular optical coherence tomography (OCT), is the most important determinant of plaque rupture in the coronary arteries. Statin treatment increases FCT and thus reduces the likelihood of acute coronary events. However, substantial statin-related FCT increase occurs in only a subset of patients. Currently, there are no methods to predict which patients will benefit. We use transcriptomic data from a clinical trial of rosuvastatin to predict if a patient's FCT will increase in response to statin therapy. MethodsFCT was measured using OCT in 69 patients at (1) baseline and (2) after 8–10 weeks of 40 mg rosuvastatin. Peripheral blood mononuclear cells were assayed via microarray. We constructed machine learning models with baseline gene expression data to predict change in FCT. Finally, we ascertained the biological functions of the most predictive transcriptomic markers. FindingsMachine learning models were able to predict FCT responders using baseline gene expression with high fidelity (Classification AUC = 0.969 and 0.972). The first model (elastic net) using 73 genes had an accuracy of 92.8%, sensitivity of 94.1%, and specificity of 91.4%. The second model (KTSP) using 18 genes has an accuracy of 95.7%, sensitivity of 94.3%, and specificity of 97.1%. We found 58 enriched gene ontology terms, including many involved with immune cell function and cholesterol biometabolism. InterpretationIn this pilot study, transcriptomic models could predict if FCT increased following 8–10 weeks of rosuvastatin. These findings may have significance for therapy selection and could supplement invasive imaging modalities.

Coronary Atherosclerotic Phenotype and Plaque Healing in Patients With Recurrent Acute Coronary Syndromes Compared With Patients With Long-term Clinical Stability

At one end of the coronary artery disease (CAD) spectrum, there are patients with multiple recurrent acute coronary syndromes (rACS), and at the other end there are those with long-standing clinical stability. Predicting the natural history of these patients is challenging because unstable plaques often heal without resulting in ACS. To assess in vivo the coronary atherosclerotic phenotype as well as the prevalence and characteristics of healed coronary plaques by optical coherence tomography (OCT) imaging in patients at the extremes of the CAD spectrum. This is an observational, single-center cohort study with prospective clinical follow-up. From a total of 823 consecutive patients enrolled in OCT Registry of the Fondazione Policlinico A. Gemelli-IRCCS, Rome, Italy, from March 2009 to February 2016, 105 patients were included in the following groups: (1) patients with rACS, defined as history of at least 3 acute myocardial infarctions (AMIs) or at least 4 ACS with at least 1 AMI; (2) patients with long-standing stable angina pectoris (ls-SAP), defined as a minimum 3-year history of stable angina; and (3) patients with a single unheralded AMI followed by a minimum 3-year period of clinical stability (sAMI). Data were analyzed from January to August 2018. Intracoronary OCT imaging of nonculprit coronary segments. Coronary plaque features and the prevalence of healed coronary plaques in nonculprit segments as assessed by intracoronary OCT imaging. Of 105 patients, 85 were men (81.0%); the median (interquartile range) age was 68 (63-75) years. Median (interquartile range) time of clinical stability was 9 (5.0-15.0) years in the ls-SAP group and 8 (4.5-14.5) years in the sAMI group. Patients in the rACS and sAMI groups showed similar prevalence of lipid-rich plaque and thin-cap fibroatheroma, which was significantly higher than in those with ls-SAP (lipid-rich plaque 80.0% [n = 24 of 30] vs 76.3% [n = 29 of 38] vs 37.8% [n = 14 of 37], respectively; P < .001; thin-cap fibroatheroma 40.0% [n = 12 of 30] vs 34.2% [n = 13 of 38] vs 8.1% [n = 3 of 37], respectively; P = .006). Spotty calcifications were more frequently observed in patients with rACS than in those with ls-SAP and sAMI (70.0% [n = 21 of 30] vs 40.5% [n = 15 of 37] vs 44.7% [n = 17 of 38], respectively; P = .04). Healed coronary plaques were rarely observed in patients with rACS, whereas their prevalence was significantly higher in patients with ls-SAP and sAMI (3.3% [n = 1 of 30] vs 29.7% [n = 11 of 37] vs 28.9% [n = 11 of 38], respectively; P = .01). Patients with rACS have a distinct atherosclerotic phenotype compared with those with ls-SAP, including higher prevalence of thin-cap fibroatheroma and lower prevalence of healed coronary plaques, suggesting that atherosclerotic profile and plaque healing may play a role in leading the natural history of patients with CAD.

Automatic multi-region segmentation of intracoronary optical coherence tomography images based on neutrosophic theory

Optical coherence tomography (OCT) has become a key technique in the diagnosis of coronary artery stenosis, which can identify plaques and vulnerable plaques in the image. Therefore, this technique is of great significance for the diagnosis of coronary heart disease. However, there is still a lack of automatic, multi-region, high-precision segmentation algorithms for coronary OCT images in the current research field. Therefore, this paper proposes a multi-zone, fully automated segmentation algorithm for coronary OCT images based on neutrosophic theory, which achieves high-precision segmentation of fibrous plaques and lipid regions. In this paper, the method of transforming OCT images into T in the area of neutrosophics is redefined based on the membership function, and the segmentation accuracy of fiber plaques is improved. For the segmentation of lipid regions, the algorithm adds homomorphic filter enhancement images, and uses OCT to transform OCT images into I in the field of neutrosophics, and further uses morphological methods to achieve high-precision segmentation. In this paper, 40 OCT images from 9 patients with typical plaques were analyzed and compared with the results of manual segmentation by doctors. Experiments show that the proposed algorithm avoids the over-segmentation and under-segmentation problems of the traditional neutrosophic theory method, and accurately segment the patch area. Therefore, the work of this paper can effectively improve the accuracy of segmentation of plaque for doctors, and assist clinicians in the diagnosis and treatment of coronary heart disease.

Automatic Segmentation Methods Based on Machine Learning for Intracoronary Optical Coherence Tomography Image

Cardiovascular diseases are closely associated with sudden rupture of atherosclerotic plaques. Previous image modalities such as magnetic resonance imaging (MRI) and intravascular ultrasound (IVUS) were unable to identify vulnerable plaques due to their limited resolution. Optical coherence tomography (OCT) is an advanced intravascular imaging technique developed in recent years which has high resolution approximately 10 microns and could provide more accurate morphology of coronary plaque. In particular, it is now possible to identify plaques with fibrous cap thickness < 65 µm, an accepted threshold value for vulnerable plaques. However, the current segmentation of OCT images are still performed manually by physicians and the process is time consuming. Automatic segmentation and recognition of vulnerable plaques through quantification of plaque components have great clinical significance for cardiovascular research. Two segmentation methods for intracoronary OCT image based on support vector machine (SVM) and convolutional neural network (CNN) were performed to detect the lumen borders and characterize the plaque component. In vivo IVUS and OCT coronary plaque data from 5 patients were acquired at Emory University with patient’s consent obtained. Seventy-seven matched IVUS and OCT slices with good image quality and medium to large lipid cores were selected for our segmentation study. Manual OCT segmentation was performed by experts and used as gold standard in the automatic segmentations. VH-IVUS was used as references and guide by the experts in the manual segmentation process. Three plaque component tissue classes were identified from OCT images in this work: lipid tissue (LT), fibrous tissue (FT) and background (BG). Procedures using two machine learning methods (CNN and SVM) were developed to segment OCT images, respectively. For CNN method, the U-Net architecture was selected due to its good performance in very different biomedical segmentation and very few annotated images. For SVM method, local binary patterns (LBPs), gray level co-occurrence matrices (GLCMs) which contains contrast, correlation, energy and homogeneity, entropy and mean value were calculated as features and assembled to feed SVM classifier. The accuracies of two segmentation methods were evaluated and compared using the OCT dataset. Segmentation accuracy is defined as the ratio of the number of pixels correctly classified over the total number of pixels. The overall classification accuracy based CNN method reached 95.8%, and the accuracies for LT, FT and BG were 86.8%, 83.4%, and 98.2%, respectively. The overall classification accuracy based SVM was 71.9%, and per-class accuracy for LT, FT and BG was 75.4%, 78.3%, and 67.0%, respectively. The two methods proposed can automatically detect the lumen borders and characterize the composition of the superficial plaque in OCT images and greatly reduce the time spent by doctors in segmenting and identifying plaques. CNN provided better segmentation accuracies compared to those achieved by SVM. Acknowledgement: This research was supported in part by NIH grant R01 EB004759, National Sciences Foundation of China grant 11672001 and a Jiangsu Province Science and Technology Agency grant BE2016785.

Intra-Slice Motion Correction of Intravascular OCT Images Using Deep Features

Intra-slice motion correction is an important step for analyzing volume variations and pathological formations from intravascular imaging. Optical coherence tomography (OCT) has been recently introduced for intravascular imaging and assessment of coronary artery disease. Two-dimensional (2-D) cross-sectional OCT images of coronary arteries play a crucial role to characterize the internal structure of the tissues. Adjacent images could be compounded; however, they might not fully match due to motion, which is a major hurdle for analyzing longitudinally each tissue in 3-D. The aim of this study is to develop a robust tissue-matching-based motion correction approach from a sequence of 2-D intracoronary OCT images. Our motion correction technique is based on the correlation between deep features obtained from a convolutional neural network (CNN) for each frame of a sequence. The optimal transformation of each frame is obtained by maximizing the similarity between the tissues of reference and moving frames. The results show a good alignment of the tissues after applying CNN features and determining the transformation parameters.

Absence of adventitial vasa vasorum formation at the coronary segment with myocardial bridge - An optical coherence tomography study.

Myocardial bridge (MB) is a myocardial bundle through which coronary segment tunnels and could compress coronary arteries causing myocardial ischemia. However, the characteristic structural findings of MB remain to be fully elucidated. Recently, we demonstrated that optical coherence tomography (OCT) enables us to visualize adventitial vasa vasorum (VV) formation in humans. In this study, we examined adventitial VV formation at the coronary segment with MB in humans using OCT. We examined 15 consecutive patients with suspected angina pectoris and MB in the left anterior descending (LAD) coronary arteries but no angiographic coronary stenosis. MB was detected on coronary angiography as a segment with milking effect. We performed intracoronary OCT imaging along the entire LAD. Morphometric analysis was performed at MB and proximal/distal segments at every 1mm. OCT examination showed the absence of adventitial VV formation at MB in the LAD, while VV was clearly noted at both the proximal and distal reference segments. Adventitial VV area was significantly less at MB compared with the proximal or distal references. These results with OCT imaging indicate that coronary segments with MB lack adventitial VV formation in humans, suggesting that MB could influence morphological and functional changes of the coronary artery.

Automated Segmentation of Bioresorbable Vascular Scaffold Struts in Intracoronary Optical Coherence Tomography Images.

Bioresorbable vascular scaffolds (BVS), the next step in the continuum of minimally invasive vascular interventions present new opportunities for patients and clinicians but challenges as well. As they are comprised of polymeric materials standard imaging is challenging. This is especially problematic as modalities like optical coherence tomography (OCT) become more prevalent in cardiology. OCT, a light-based intracoronary imaging technique, provides cross-sectional images of plaque and luminal morphology. Until recently segmentation of OCT images for BVS struts was performed manually by experts. However, this process is time consuming and not tractable for large amounts of patient data. Several automated methods exist to segment metallic stents, which do not apply to the newer BVS. Given this current limitation coupled with the emerging popularity of the BVS technology, it is crucial to develop an automated methodology to segment BVS struts in OCT images. The objective of this paper is to develop a novel BVS strut detection method in intracoronary OCT images. First, we preprocess the image to remove imaging artifacts. Then, we use a K-means clustering algorithm to automatically segment the image. Finally, we isolate the stent struts from the rest of the image. The accuracy of the proposed method was evaluated using expert estimations on 658 annotated images acquired from 7 patients at the time of coronary arterial interventions. Our proposed methodology has a positive predictive value of 0.93, a Pearson Correlation coefficient of 0.94, and a F1 score of 0.92. The proposed methodology allows for rapid, accurate, and fully automated segmentation of BVS struts 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

PurposeQuantitative and automatic analysis of intracoronary optical coherence tomography images is useful and time-saving to assess cardiovascular risk in the clinical arena.MethodsFirst, 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.ResultsThe 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 29 pm 46~upmu text {m} for the intima–media interface, 30 pm 50~upmu text {m} for the media–adventitia interface, and 50 pm 64~upmu text {m} 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).ConclusionThe 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.

Plaque segmentation of intracoronary optical coherence tomography images based on K-means and improved random walk algorithm

In recent years, optical coherence tomography (OCT) has developed into a popular coronary imaging technology at home and abroad. The segmentation of plaque regions in coronary OCT images has great significance for vulnerable plaque recognition and research. In this paper, a new algorithm based on K-means clustering and improved random walk is proposed and Semi-automated segmentation of calcified plaque, fibrotic plaque and lipid pool was achieved. And the weight function of random walk is improved. The distance between the edges of pixels in the image and the seed points is added to the definition of the weight function. It increases the weak edge weights and prevent over-segmentation. Based on the above methods, the OCT images of 9 coronary atherosclerotic patients were selected for plaque segmentation. By contrasting the doctor's manual segmentation results with this method, it was proved that this method had good robustness and accuracy. It is hoped that this method can be helpful for the clinical diagnosis of coronary heart disease.

Simultaneous subacute coronary artery stent thrombosis in a carrier of two CYP2C19 loss–of function polymorphisms (*2/*3)

Simultaneous subacute coronary artery stent thrombosis in a carrier of two CYP2C19 loss–of function polymorphisms (*2/*3)

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

ObjectivesThe 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. BackgroundAlthough 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. MethodsWe 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. ResultsHigh-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. ConclusionsThis 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.