Optical Coherence Tomography Frames Research Articles

Tensor Ring Decomposition Guided Dictionary Learning for OCT Image Denoising.

Optical coherence tomography (OCT) is a non-invasive and effective tool for the imaging of retinal tissue. However, the heavy speckle noise, resulting from multiple scattering of the light waves, obscures important morphological structures and impairs the clinical diagnosis of ocular diseases. In this paper, we propose a novel and powerful model known as tensor ring decomposition-guided dictionary learning (TRGDL) for OCT image denoising, which can simultaneously utilize two useful complementary priors, i.e., three-dimensional low-rank and sparsity priors, under a unified framework. Specifically, to effectively use the strong correlation between nearby OCT frames, we construct the OCT group tensors by extracting cubic patches from OCT images and clustering similar patches. Then, since each created OCT group tensor has a low-rank structure, to exploit spatial, non-local, and its temporal correlations in a balanced way, we enforce the TR decomposition model on each OCT group tensor. Next, to use the beneficial three-dimensional inter-group sparsity, we learn shared dictionaries in both spatial and temporal dimensions from all of the stacked OCT group tensors. Furthermore, we develop an effective algorithm to solve the resulting optimization problem by using two efficient optimization approaches, including proximal alternating minimization and the alternative direction method of multipliers. Finally, extensive experiments on OCT datasets from various imaging devices are conducted to prove the generality and usefulness of the proposed TRGDL model. Experimental simulation results show that the suggested TRGDL model outperforms state-of-the-art approaches for OCT image denoising both qualitatively and quantitatively.

High-resolution rectoscopy using MHz optical coherence tomography: a step towards real time 3D endoscopy

Colonoscopy and endoscopic ultrasound play pivotal roles in the assessment of rectal diseases, especially rectal cancer and inflammatory bowel diseases. Optical coherence tomography (OCT) offers a superior depth resolution, which is a critical factor for individualizing the therapeutic concept and evaluating the therapy response. We developed two distinct rectoscope prototypes, which were integrated into a 1300 nm MHz-OCT system constructed at our facility. The rapid rotation of the distal scanning probe at 40,000 revolutions per minute facilitates a 667 Hz OCT frame rate, enabling real-time endoscopic imaging of large areas. The performance of these OCT-rectoscopes was assessed in an ex vivo porcine colon and a post mortem human in-situ colon. The OCT-rectoscope consistently distinguished various layers of the intestinal wall, identified gut-associated lymphatic tissue, and visualized a rectal polyp during the imaging procedure with 3D-reconstruction in real time. Subsequent histological examination confirmed these findings. The body donor was preserved using an ethanol-glycerol-lysoformin-based technique for true-to-life tissue consistency. We could demonstrate that the novel MHZ-OCT-rectoscope effectively discriminates rectal wall layers and crucial tissue characteristics in a post mortem human colon in-situ. This real-time-3D-OCT holds promise as a valuable future diagnostic tool for assessing disease state and therapy response on-site in rectal diseases.

Fully automated construction of three-dimensional finite element simulations from Optical Coherence Tomography

Despite recent advances in diagnosis and treatment, atherosclerotic coronary artery diseases remain a leading cause of death worldwide. Various imaging modalities and metrics can detect lesions and predict patients at risk; however, identifying unstable lesions is still difficult. Current techniques cannot fully capture the complex morphology-modulated mechanical responses that affect plaque stability, leading to catastrophic failure and mute the benefit of device and drug interventions. Finite Element (FE) simulations utilizing intravascular imaging OCT (Optical Coherence Tomography) are effective in defining physiological stress distributions. However, creating 3D FE simulations of coronary arteries from OCT images is challenging to fully automate given OCT frame sparsity, limited material contrast, and restricted penetration depth. To address such limitations, we developed an algorithmic approach to automatically produce 3D FE-ready digital twins from labeled OCT images. The 3D models are anatomically faithful and recapitulate mechanically relevant tissue lesion components, automatically producing morphologies structurally similar to manually constructed models whilst including more minute details. A mesh convergence study highlighted the ability to reach stress and strain convergence with average errors of just 5.9% and 1.6% respectively in comparison to FE models with approximately twice the number of elements in areas of refinement. Such an automated procedure will enable analysis of large clinical cohorts at a previously unattainable scale and opens the possibility for in-silico methods for patient specific diagnoses and treatment planning for coronary artery disease.

Abstract 622: Automated Platform For Large Scale Structural Simulations Of Atherosclerotic Lesions

Coronary artery disease persists as the world’s leading cause of death and often associated with atherosclerotic plaque disruption in the face of excessive plaque structure stress (PSS). Lesion tolerance of PSS correlates with the material and morphological properties of the arterial constituents. 2D-Finite Element Analysis (FEA) performed on models generated from arterial images (e.g. optical coherence tomography (OCT)) has procured a deeper understanding of lesion biomechanics but not the full complex 3D morphological-mechanical response. OCT frame sparsity and manual burden associated with 3D model generation have continuously plagued 3D simulations and automated large-scale studies. We present a fully automated software platform to determine 3D arterial stress distributions from deep-learning labeled OCT images of complex lesions by interpolating labeled frames. A preliminary study showed that frames with a high PSS (over 150 kPa) had significantly larger lipid arc angles, lumen-plaque eccentricity, lipid area, and calcium area, mirroring clinical observations. Such promising results validate the potential for the platform to be utilized for plethora of applications from unprecedented large scale in-silico studies to enhance clinical metrics, optimization of patient-specific angiography operations, and training data for emerging AI simulations.

An All-in-One Tool for 2D Atherosclerotic Disease Assessment and 3D Coronary Artery Reconstruction.

Diagnosis of coronary artery disease is mainly based on invasive imaging modalities such as X-ray angiography, intravascular ultrasound (IVUS) and optical coherence tomography (OCT). Computed tomography coronary angiography (CTCA) is also used as a non-invasive imaging alternative. In this work, we present a novel and unique tool for 3D coronary artery reconstruction and plaque characterization using the abovementioned imaging modalities or their combination. In particular, image processing and deep learning algorithms were employed and validated for the lumen and adventitia borders and plaque characterization at the IVUS and OCT frames. Strut detection is also achieved from the OCT images. Quantitative analysis of the X-ray angiography enables the 3D reconstruction of the lumen geometry and arterial centerline extraction. The fusion of the generated centerline with the results of the OCT or IVUS analysis enables hybrid coronary artery 3D reconstruction, including the plaques and the stent geometry. CTCA image processing using a 3D level set approach allows the reconstruction of the coronary arterial tree, the calcified and non-calcified plaques as well as the detection of the stent location. The modules of the tool were evaluated for efficiency with over 90% agreement of the 3D models with the manual annotations, while a usability assessment using external evaluators demonstrated high usability resulting in a mean System Usability Scale (SUS) score equal to 0.89, classifying the tool as "excellent".

Diagnosis of coronary layered plaque by deep learning

Healed coronary plaques, morphologically characterized by a layered phenotype, are signs of previous plaque destabilization and healing. Recent optical coherence tomography (OCT) studies demonstrated that layered plaque is associated with higher levels of local and systemic inflammation and rapid plaque progression. However, the diagnosis of layered plaque needs expertise in OCT image analysis and is susceptible to inter-observer variability. We developed a deep learning (DL) model for an accurate diagnosis of layered plaque. A Visual Transformer (ViT)-based DL model that integrates information from adjacent frames emulating the cardiologists who review consecutive OCT frames to make a diagnosis was developed and compared with the standard convolutional neural network (CNN) model. A total of 237,021 cross-sectional OCT images from 581 patients collected from 8 sites were used for training and internal validation, and 65,394 images from 292 patients collected from another site were used for external validation. In the five-fold cross-validation, the ViT-based model provided better performance (area under the curve [AUC]: 0.860; 95% confidence interval [CI]: 0.855–0.866) than the standard CNN-based model (AUC: 0.799; 95% CI: 0.792–0.805). The ViT-based model (AUC: 0.845; 95% CI: 0.837–0.853) also surpassed the standard CNN-based model (AUC: 0.791; 95% CI: 0.782–0.800) in the external validation. The ViT-based DL model can accurately diagnose a layered plaque, which could help risk stratification for cardiac events.

Diagnosis of coronary layered plaque by deep learning

Abstract Background/Introduction Healed coronary plaques, morphologically characterized by a layered pattern, are signatures of previous plaque disruption and healing. Recent optical coherence tomography (OCT) studies showed that layered plaque is associated with vascular vulnerability and rapid plaque progression. However, the diagnosis of layered plaque requires expertise in OCT image interpretation and is susceptible to interobserver variability. Purpose We aimed to develop a deep learning (DL) model for an accurate diagnosis of layered plaque. Methods We developed a Visual Transformer (ViT)-based DL model emulating the cardiologists who review consecutive OCT frames to make a diagnosis (Figure 1), and compared it to the standard convolutional neural network (CNN) model. We used 302,415 cross-sectional OCT images from 873 patients collected from 9 sites: 237,021 images from 581 patients for training and internal validation from 8 sites, and 65394 images from 292 patients collected from another site for external validation. Results Model performances were evaluated using the area under the receiver operating characteristics (AUC). In the five-fold cross validation, the ViT-based model showed better performance than the standard CNN-based model with AUC of 0.886 (95% confidence interval [CI], 0.882–0.891) compared with 0.797 (95% CI, 0.790–0.804). The ViT-based model also outperformed the standard CNN-based model in the external validation, with an AUC of 0.857 (95% CI, 0.849–0.864) compared to 0.806 (95% CI, 0.797–0.815) (Figure 2). Conclusion(s) The ViT-based DL model will help cardiologists to make an accurate diagnosis of layered plaque, which might help to stratify the risk of future adverse cardiac events. Funding Acknowledgement Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Mrs. Gillian Gray through the Allan Gray Fellowship Fund in Cardiology

Interobserver Variability Between Expert, Experienced, and Novice Operator Affects Interpretation of Optical Coherence Tomography and 20 MHz Intravascular Ultrasound Imaging

BackgroundVessel sizing and calcium detection by intracoronary imaging informs optimal strategy during coronary interventions, but image interpretation and analysis software vary considerably between platforms. We compared the interobserver variability of clinicians with a range of experience in assessing co-registered optical coherence tomography (OCT) and 20 MHz solid state intravascular ultrasound (IVUS) vessel/ lumen geometry and quantitative plaque data. MethodsCo-registered OCT and IVUS frames at the minimum lumen area (MLA) and 5 frames at 2 mm intervals upstream and downstream were read blinded by an expert, consultant, interventional fellow and registrar to define vessel and lumen sizes, plaque characteristics (arc of calcium and lipid) and presence of OCT-defined thin-capped fibroatheroma (TCFA). ResultsOverall, 143 paired frames of OCT and IVUS were analysed. Excellent consistency was seen for all OCT measures of vessel/luminal geometry irrespective of experience (all intraclass correlation coefficients (ICC) >0.89). Inexperience compromised ICC for IVUS (lumen area ICC 0.56; vessel size ICC 0.65) and overestimated lumen size compared to expert (p < 0.001). Calcium arc agreement for OCT and IVUS was equally strong for all but the most inexperienced reader (ICC >0.84). OCT lipid arc agreement was moderate for experienced readers but poor for all grades of reader with IVUS (ICC 0.24–0.43). OCT-TCFA agreement was moderate between expert and consultant (κ = 0.55) and poor for less experienced readers. ConclusionOCT dimensions are minimally affected by observer experience and more consistent than IVUS. Inexperienced readers oversize with IVUS. Calcium arc is dependably assessed by IVUS and OCT by all but the most inexperienced reader.

Histopathology-Based Deep-Learning Predicts Atherosclerotic Lesions in Intravascular Imaging.

Background: Optical coherence tomography is a powerful modality to assess atherosclerotic lesions, but detecting lesions in high-resolution OCT is challenging and requires expert knowledge. Deep-learning algorithms can be used to automatically identify atherosclerotic lesions, facilitating identification of patients at risk. We trained a deep-learning algorithm (DeepAD) with co-registered, annotated histopathology to predict atherosclerotic lesions in optical coherence tomography (OCT).Methods: Two datasets were used for training DeepAD: (i) a histopathology data set from 7 autopsy cases with 62 OCT frames and co-registered histopathology for high quality manual annotation and (ii) a clinical data set from 51 patients with 222 OCT frames in which manual annotations were based on clinical expertise only. A U-net based deep convolutional neural network (CNN) ensemble was employed as an atherosclerotic lesion prediction algorithm. Results were analyzed using intersection over union (IOU) for segmentation.Results: DeepAD showed good performance regarding the prediction of atherosclerotic lesions, with a median IOU of 0.68 ± 0.18 for segmentation of atherosclerotic lesions. Detection of calcified lesions yielded an IOU = 0.34. When training the algorithm without histopathology-based annotations, a performance drop of >0.25 IOU was observed. The practical application of DeepAD was evaluated retrospectively in a clinical cohort (n = 11 cases), showing high sensitivity as well as specificity and similar performance when compared to manual expert analysis.Conclusion: Automated detection of atherosclerotic lesions in OCT is improved using a histopathology-based deep-learning algorithm, allowing accurate detection in the clinical setting. An automated decision-support tool based on DeepAD could help in risk prediction and guide interventional treatment decisions.

First in‐human evaluation of a novel intravascular ultrasound and optical coherence tomography system for intracoronary imaging

AbstractAimsWe evaluated the first in‐human performance of a novel hybrid imaging catheter that permits simultaneous and co‐registered acquisition of intravascular ultrasound (IVUS) and optical coherence tomography (OCT) images.Methods and ResultsA total of 17 patients undergoing planned percutaneous coronary intervention (PCI) were imaged between August 2018 and August 2019. Eleven patients with both pre‐ and post‐PCI IVUS and OCT images were included in the offline image analysis. IVUS and OCT images were analyzed separately then together with co‐registered images for pre‐stent findings, and only separately for post‐stent findings. A total of 926 frames were analyzed (218 pre‐PCI, 708 post‐PCI). There was substantial agreement to detect calcific plaque between co‐registered IVUS‐OCT and standalone IVUS (Kappa 0.72 [0.65–0.79]) and standalone OCT (Kappa 0.75 [0.68–0.81]) while standalone imaging modalities showed lower agreement to detect lipidic and fibrotic plaques compared with co‐registered IVUS‐OCT. There were more frames with stent underexpansion on IVUS than OCT [72 (28.7%) vs. 58 (23.1%), respectively, p = 0.039]. Detection rates of incomplete stent apposition (present on 20 OCT frames vs. 2 IVUS frames, p &lt; 0.001) and tissue protrusion (40 vs. 27 frames, p &lt; 0.001) were higher on OCT than IVUS. One stent edge dissection was detected in the image analysis and was seen on OCT but not IVUS. All 177 frames with image artifacts contained at least one co‐registered imaging modality with interpretable diagnostic content. There were no study device‐related adverse events.ConclusionsHybrid image acquisition was safe. The availability of both IVUS and OCT changed image interpretation compared to either modality alone, suggesting a complementary role of these two techniques.

Clinical implications of coronary artery morphology of patients with ischemia and non-obstructive coronary artery disease (INOCA) -An intracoronary OCT study-

Abstract Introduction Ischemia and non-obstructive coronary artery disease (INOCA), including microvascular spasm (MVS) and epicardial spasm, has recently attracted much attention, for which in vivo imaging evaluation for coronary artery morphology is warranted for better understanding of this disorder. Besides the improved diagnostic accuracy of optical coherence tomography (OCT) for coronary plaques, we have recently demonstrated its capability for in vivo visualization of coronary adventitial vasa vasorum (VV) and the enhanced VV formation in patients with epicardial spasm. Purpose We aimed to examine OCT-delineated morphological characteristics in patients with INOCA in vivo. Methods A total of 335 consecutive INOCA patients, who underwent pharmacological spasm provocation tests, lactate sampling, and OCT imaging over the entire length of the left anterior descending (LAD) coronary arteries, were enrolled at our institute over 68 months from April 2013. They were classified into 4 groups; control with non-cardiac chest pain, MVS, diffuse spasm (DS), or focal spasm (FS) (Fig. 1A). MVS was diagnosed when negative lactate extraction ratio (coronary orifice &amp;lt; coronary sinus) was detected despite the absence of epicardial spasm during the spasm provocation test. DS was defined as epicardial spasm induced in more than 2 coronary segments in LAD, and FS as epicardial spasm in one segment. Quantitative analyses for adventitial inflammation and atherosclerotic changes were performed by calculating VV density and %area stenosis (AS) on OCT (Fig. 1B, E). Furthermore, index of microcirculatory resistance (IMR), a marker of microvascular disorder with a cut-off value of ≥25, was measured during intravenous infusion of adenosine, which was then correlated with VV densities in the MVS and DS groups. Coronary plaque with a necrotic core was classified as fibroatheroma (FA), and the number of OCT frames with internal VV (IVV) in the atheroma was counted. Results VV density was significantly higher in MVS as compared with the controls (Fig. 1B). DS was most prevalent in INOCA (Fig. 1A) with highest VV density (Fig. 1B). Patients with IMR≥25 were predominantly distributed with a gradual increase in the MVS, DS, and FS groups, but none in the controls (Fig. 1C). Importantly, there was a significant positive correlation between VV densities and IMR in the MVS and DS groups (Fig. 1D). In addition, FS had the largest plaque size and showed the highest prevalence of FA and IVV (Fig. 1E–G). Conclusions These results indicate that MVS and DS are characterized by vasomotion abnormalities associated with adventitial inflammation and microvascular disorder, while FS by vulnerable atherosclerotic phenotype, suggesting that OCT may be useful for screening high-risk populations in INOCA. Figure 1 Funding Acknowledgement Type of funding source: None

Side branch FFR after provisional stenting: simplified approach based on OCT frame count

Abstract Background/Introduction Treatment of bifurcation coronary artery lesions remains a major challenge in interventional cardiology. Side branch (SB) stenoses are frequently observed after stent implantation in bifurcation lesions, although angiographically narrowed SBs may not be functionally significant. Fractional flow reserve (FFR), a pressure-derived index of the hemodynamic significance of a coronary artery stenosis, may be useful in determining whether additional intervention is required in jailed SBs. Angiography and intravascular ultrasound (IVUS) derived parameters have showed poor diagnostic accuracy in predicting the functional significance of jailed SBs. Purpose The aim of the present study was to use high resolution optical coherence tomography (OCT) imaging to predict functionally significant SB stenoses after provisional stenting defined as SB FFR ≤0.80. Methods Seventy-one patients with 71 calcified bifurcation lesions with angiographically intermediate SB stenoses undergoing provisional stenting were enrolled in the prospective study. OCT pullbacks were performed before and after stent placement, and SB FFR was measured after main vessel stenting. SB ostium area (SBOA) was assessed using three-dimensional OCT cut-plane analysis off-line. In addition, we developed a simplified approach to SB ostium assessment based on SB ostium frame count using two-dimensional OCT pullback not requiring off-line 3D reconstruction. For the analysis, consecutive frames were counted between the most distal and most proximal take-off of the SB frames. Results Similar to previous studies, quantitative coronary angiography findings were not associated with the functional significance of SBs after main vessel stenting. In contrast, SBOA assessed by 3D-OCT after provisional stenting strongly correlated with post-procedure SB FFR. The optimal cut-off value for the SBOA area to predict a SB FFR ≤0.80 was 0.76 mm2 (sensitivity 82%, specificity 89% and area under the curve of 0.92 (95% CI: 0.84–0.99). A simplified approach to SB ostium assessment using OCT frame count yielded a sensitivity of 82%, specificity 89% and area under the curve 0.92 (95% CI: 0.84 to 0.99) with a cut-off of 4.5 frames allowing detection of functionally significant SB stenoses during the procedure in real time. Figure 1 shows a receiver-operating characteristic curve for SB FFR ≤0.8 and a representative case with SB FFR = 0.66 after provisional stenting and SB ostium frame count equal 3 (Frame 1 to 3) Conclusion(s) Assessment of SB using either 3D OCT off-line reconstruction or a simplified approach based on OCT frame count can detect SB branches with FFR ≤0.80 with high sensitivity and specificity. The developed approaches may represent a useful tool to assess provisional stent outcomes. Figure 1 Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): Boston Scientific; St. Jude Medical

3D reconstruction of coronary artery bifurcations from coronary angiography and optical coherence tomography: feasibility, validation, and reproducibility

The three-dimensional (3D) representation of the bifurcation anatomy and disease burden is essential for better understanding of the anatomical complexity of bifurcation disease and planning of stenting strategies. We propose a novel methodology for 3D reconstruction of coronary artery bifurcations based on the integration of angiography, which provides the backbone of the bifurcation, with optical coherence tomography (OCT), which provides the vessel shape. Our methodology introduces several technical novelties to tackle the OCT frame misalignment, correct positioning of the OCT frames at the carina, lumen surface reconstruction, and merging of bifurcation lumens. The accuracy and reproducibility of the methodology were tested in n = 5 patient-specific silicone bifurcations compared to contrast-enhanced micro-computed tomography (µCT), which was used as reference. The feasibility and time-efficiency of the method were explored in n = 7 diseased patient bifurcations of varying anatomical complexity. The OCT-based reconstructed bifurcation models were found to have remarkably high agreement compared to the µCT reference models, yielding r2 values between 0.91 and 0.98 for the normalized lumen areas, and mean differences of 0.005 for lumen shape and 0.004 degrees for bifurcation angles. Likewise, the reproducibility of our methodology was remarkably high. Our methodology successfully reconstructed all the patient bifurcations yielding favorable processing times (average lumen reconstruction time < 60 min). Overall, our method is an easily applicable, time-efficient, and user-friendly tool that allows accurate and reproducible 3D reconstruction of coronary bifurcations. Our technique can be used in the clinical setting to provide information about the bifurcation anatomy and plaque burden, thereby enabling planning, education, and decision making on bifurcation stenting.

Detection of optical coherence tomography-defined thin-cap fibroatheroma in the coronary artery using deep learning.

The aim of this study was to develop a deep learning model for classifying frames with versus without optical coherence tomography (OCT)-derived thin-cap fibroatheroma (TCFA). A total of 602 coronary lesions from 602 angina patients were randomised into training and test sets in a 4:1 ratio. A DenseNet model was developed to classify OCT frames with or without OCT-derived TCFA. Gradient-weighted class activation mapping was used to visualise the area of attention. In the training sample (35,678 frames of 480 lesions), the model with fivefold cross-validation had an overall accuracy of 91.6±1.7%, sensitivity of 88.7±3.4%, and specificity of 91.8±2.0% (averaged AUC=0.96±0.01) in predicting the presence of TCFA. In the test samples (9,722 frames of 122 lesions), the overall accuracy at the frame level was 92.8% within the lesion (AUC=0.96) and 91.3% in the entire OCT pullback. The correlation between the %TCFA burden per vessel predicted by the model compared with that identified by experts was significant (r=0.87, p<0.001). The region of attention was localised at the site of the thin cap in 93.4% of TCFA-containing frames. Total computational time per pullback was 2.1±0.3 seconds. A deep learning algorithm can accurately detect an OCT-TCFA with high reproducibility. The time-saving computerised process may assist clinicians to recognise high-risk lesions easily and to make decisions in the catheterisation laboratory.

Assessment of Cardiac Allograft Vasculopathy from Oct Images: Automated Analysis is as Good as Expert-Guided Approach

Cardiac Allograft Vasculopathy (CAV) is a frequent complication after heart transplantation (HTx). To help identify patients at risk of CAV, 3D quantitative analysis of coronary wall thickening is of major importance. Until now, substantial manual tracing effort was required. We report a fully automated approach using optical coherence tomography (OCT) imaging. Lumen surface, intimal and medial layers were identified using our LOGISMOS segmentation framework. Coronary wall regions with layered appearance were automatically identified in each OCT frame using deep learning. These segmentation and classification approaches were newly combined in one fully automated system. The comparison between fully automated and expert-guided analyses was performed in 116 patients with OCT imaging at 1 and 12 months after HTx (1M, 12M). The expert-guided analysis used our previously-reported semi-automated layer segmentation method with expert-identified analyzable regions thus yielding an independent standard. The table shows that our fully automated approach yields quantitative indices of CAV progression that are statistically indistinguishable from the independent standard using Wilcoxon signed-rank test. The observed measurement errors are very small fractions of the OCT pixel size (∼10 μm/pixel). While the expert-guided definition of the independent standard required 2-15 minutes of user-guided segmentation and at least 30 minutes of manual definition of layered wall regions per pullback, the new automated analysis required less than 2 minutes of computer processing for each coronary pullback (median length 38.7 mm). Our approach is fully automated, efficient, and facilitates accurate layer-specific quantification of coronary wall thickness and their changes 1M-12M after HTx. The fully automated method can replace the manual or expert-guided analyses of OCT image sequences while maintaining high levels of accuracy.

Association of Intimal Neovessels Noted by Optical Coherence Tomography with Cardiac Allograft Vasculopathy.

BackgroundCardiac allograft vasculopathy (CAV) is a leading cause of graft failure in cardiac transplant recipients. Progression of intimal thickening noted during routine surveillance intracoronary imaging is associated with the development of CAV. However, mechanisms of CAV development are poorly understood and targets for therapy modification remain elusive. We investigated the association of neovessels (INs) within the intima, noted by optical coherence tomography (OCT) during routine CAV surveillance imaging, with intimal thickening and co-incident CAV.MethodsCoronary angiography and OCT images of 45 consecutive cardiac transplant recipients undergoing surveillance coronary imaging were reviewed. The presence of INs, defined as dark, tubular or rotund intimal structures, measuring 50-200 µm in diameter, noted in at least three OCT frames, was quantified. CAV diagnosis was determined by utilizing the International Society of Heart and Lung Transplant classification system. Demographic and clinical data was obtained by chart review. Significant associations between the presence of INs and CAV, intimal thickening, and demographic features were evaluated.ResultsINs were observed in 22/45 evaluated patients (49%), while angiographic CAV was observed in 24/45 patients, with a significant association noted between the presence of INs and CAV (p < 0.001). INs were also associated with increasing intimal thickness (p < 0.001), co-morbid hypertension (p = 0.010), and increasing transplant age (p= 0.002) on multivariate analysis.ConclusionINs are prevalent in cardiac transplant recipients and are significantly associated with CAV, increased intimal thickness, increasing transplant age, and co-morbid hypertension. Further investigation is warranted regarding the temporal relationship of IN development and the onset of CAV, as well as the mechanisms of IN development in this population.

Real time Fully Automated Internal Layer Segmentation of Human Retina in Optical Coherence Tomography Images

In the field of ophthalmology, optical coherence tomography (OCT) has proven to be a powerful imaging technique when it comes to diagnosing various eye-related diseases. This research article introduces a real-time automatic retinal layer segmentation algorithm based on intensity variation in the OCT images. The built algorithm is capable of detecting internal retinal layers like the internal limiting membrane (ILM), the retinal pigment epithelium (RPE) and the retinal nerve fiber layer (RNFL) with micrometer level precision, the algorithm uses openMP for parallelized computation for real-time visualization of the segmented retinal layers. The total execution time of the algorithm was evaluated using various image sizes and compared with the OCT frame rate to demonstrate the efficiency of real-time segmentation.

Fully Automated Lumen Segmentation Method for Intracoronary Optical Coherence Tomography

Background Optical coherence tomography (OCT) is an innovative imaging technique that generates high-resolution intracoronary images. In the last few years, the need for more precise analysis regarding coronary artery disease to achieve optimal treatment has made intravascular imaging an area of primary importance in interventional cardiology. One of the main challenges in OCT image analysis is the accurate detection of lumen which is significant for the further prognosis. Method In this research, we present a new approach to the segmentation of lumen in OCT images. The proposed work is focused on designing an efficient automatic algorithm containing the following steps: preprocessing (artifacts removal: speckle noise, circular rings, and guide wire), conversion between polar and Cartesian coordinates, and segmentation algorithm. Results The implemented method was tasted on 667 OCT frames. The lumen border was extracted with a high correlation compared to the ground truth: 0.97 ICC (0.97–0.98). Conclusions Proposed algorithm allows for fully automated lumen segmentation on optical coherence tomography images. This tool may be applied to automated quantitative lumen analysis.

Co-localization of plaque macrophages with calcification is associated with a more vulnerable plaque phenotype and a greater calcification burden in coronary target segments as determined by OCT.

BackgroundThe presence of plaque macrophages and microcalcifications are acknowledged features of plaque vulnerability. Experimental data suggest that microcalcifications promote inflammation and macrophages foster microcalcifications. However, co-localization of plaque macrophages and calcification (ColocCaMa) in coronary segments and its impact on plaque phenotype and lesion vulnerability is unexplored.MethodsPlaque morphology including ColocCaMa of calcified coronary target segments in patients with stable coronary artery disease (n = 116) was analyzed using optical coherence tomography (OCT) prior to coronary intervention. Therefore we considered macrophages co-localized with calcification if their distance in an OCT frame was <100μm and OCT-defined microcalcifications with a calcium arc <22.5°.ResultsColocCaMa was present in 29/116(25.0%) coronary segments. Calcium burden was greater (calcium volume index:1731±1421°*mm vs. 963±984°*mm, p = 0.002) and calcifications were more superficial (minimal thickness of the fibrous cap overlying the calcification 35±37μm vs. 64±72μm, p = 0.005) in the presence of ColocCaMa. Segments with ColocCaMa demonstrated a higher incidence of newly suggested features of plaque vulnerability, with a 3.5-fold higher number of OCT-defined microcalcifications (0.7±1.0 vs. 0.2±0.6, p = 0.022) and a 6.7-fold higher incidence of plaque inflammation (macrophage volume index:148.7±248.3°*mm vs. 22.2±57.4°*mm, p<0.001). Clinically, intima–media thickness (IMT) in carotid arteries was increased in patients with ColocCaMa (1.02±0.30mm vs. 0.85±0.18, p = 0.021).In a multivariate model, IMT (OR1.76 for 100μm, 95%CI 1.16–2.65, p = 0.007), HDL-cholesterol (OR0.36 for 10mg/dl, 95%CI 0.16–0.84, p = 0.017), calcium volume index (OR1.07 for 100°*mm, 95%CI 1.00–1.14, p = 0.049), macrophage volume index (OR5.77 for 100°*mm, 95%CI 2.04–16.3, p = 0.001) and minimal luminal area (OR3.41, 95%CI 1.49–7.78, p = 0.004) were independent predictors of ColocCaMa.ConclusionPlaque macrophages co-localize with calcifications in coronary target segments and this is associated with high-risk morphological features including microcalcifications and macrophage infiltration as well as with greater calcification burden. Our data may add to the understanding of the relationship between plaque macrophages, vascular calcification and their clinical impact.

Zinc oxide nanoparticles (ZnONPs) as contrast agent for imaging of animal tissue using swept source optical coherence tomography (SSOCT)

We present applicability of ZnO nanoparticles (ZnONPs) as a potential contrast agent for Optical Coherence Tomography (OCT) imaging. A 1064 nm laser source was used in a swept source configuration capable of a scanning rate of 100 kHz (100,000 A-lines/s). Swept Source Optical Coherence Tomography (SSOCT) is a variation of OCT, offering greater scan rates, higher resolution and signal-to-noise ratios. To enhance the contrast of an OCT frame, ZnO nanoparticles were used as an exogeneous contrast agent, which were fabricated using zinc acetic acid derivation and oxalic acid via precipitation. The particles were characterized using UV–vis Spectroscopy, Dynamic Light Scattering (DLS), Scanning Electron Microscopy (SEM) and Energy Dispersive X-Ray Spectroscopy (EDX). In-vitro imaging was performed on chicken breast tissue, with and without the application of ZnONPs. The contrast enhancements of the ZnONPs was determined at various time from the time of application, with calculation of the scattering coefficient and Contrast-to-Noise Ratio (CNR).