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

Co-registered optical coherence tomography and X-ray angiography for the prediction of fractional flow reserve

Cardiovascular disease (CVD) stands as the leading global cause of mortality, and coronary artery disease (CAD) has the highest prevalence, contributing to 42% of these fatalities. Recognizing the constraints inherent in the anatomical assessment of CAD, Fractional Flow Reserve (FFR) has emerged as a pivotal functional diagnostic metric. Herein, we assess the potential of employing an ensemble approach with deep neural networks (DNN) to predict invasively measured Fractional Flow Reserve (FFR) using raw anatomical data extracted from both optical coherence tomography (OCT) and X-ray coronary angiography (XA). In this study, we used a challenging dataset, with 46% of the lesions falling within the FFR range of 0.75 to 0.85. Despite this complexity, our model achieved an accuracy of 84.3%, demonstrating a sensitivity of 87.5% and a specificity of 81.4%. Our results demonstrate that incorporating both OCT and XA signals, co-registered, as inputs for the DNN model leads to an important increase in overall accuracy.

Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine

Personalized medicine transforms healthcare by adapting interventions to individuals' unique genetic, molecular, and clinical profiles. To maximize diagnostic and/or therapeutic efficacy, personalized medicine requires advanced imaging devices and sensors for accurate assessment and monitoring of individual patient conditions or responses to therapeutics. In the field of biomedical optics, short-wave infrared (SWIR) techniques offer an array of capabilities that hold promise to significantly enhance diagnostics, imaging, and therapeutic interventions. SWIR techniques provide in vivo information, which was previously inaccessible, by making use of its capacity to penetrate biological tissues with reduced attenuation and enable researchers and clinicians to delve deeper into anatomical structures, physiological processes, and molecular interactions. Combining SWIR techniques with machine learning (ML), which is a powerful tool for analyzing information, holds the potential to provide unprecedented accuracy for disease detection, precision in treatment guidance, and correlations of complex biological features, opening the way for the data-driven personalized medicine field. Despite numerous biomedical demonstrations that utilize cutting-edge SWIR techniques, the clinical potential of this approach has remained significantly underexplored. This paper demonstrates how the synergy between SWIR imaging and ML is reshaping biomedical research and clinical applications. As the paper showcases the growing significance of SWIR imaging techniques that are empowered by ML, it calls for continued collaboration between researchers, engineers, and clinicians to boost the translation of this technology into clinics, ultimately bridging the gap between cutting-edge technology and its potential for personalized medicine.

Reciprocal assistance of intravascular imaging in three-dimensional stent reconstruction: Using cross-modal translation based on disentanglement representation

BACKGROUND

Accurate and efficient 3-dimension (3D) reconstruction of coronary stents in intravascular imaging of optical coherence tomography (OCT) or intravascular ultrasound (IVUS) is important for optimization of complex percutaneous coronary interventions (PCI). Deep learning has been used to address this technical challenge. However, manual annotation of stent is strenuous, especially for IVUS images. To this end, we aim to explore whether the OCT and IVUS images can assist each other in stent 3D reconstruction when one of them is lack of labeled dataset.

METHODS

We firstly performed cross-modal translation between OCT and IVUS images, where disentangled representation was employed to generate synthetic images with good stent consistency. The reciprocal assistance of OCT and IVUS in stent 3D reconstruction was then conducted by applying unsupervised and semi-supervised learning with the aid of synthetic images. Stent consistency in synthetic images and reciprocal effectiveness in stent 3D reconstruction were quantitatively assessed by F1-Score (FS) on two datasets: OCT-High Definition IVUS (HD IVUS) and OCT-Conventional IVUS (IVUS).

RESULTS

The employment of disentangled representation achieved higher stent consistency in synthetic images (OCT to HD IVUS: FS=0.789 vs 0.684; HD IVUS to OCT: FS=0.766 vs 0.682; OCT to IVUS: FS=0.806 vs 0.664; IVUS to OCT: FS=0.724 vs 0.673). For stent 3D reconstruction, the assistance from synthetic images significantly promoted unsupervised adaptation across modalities (OCT to HD IVUS: FS=0.776 vs 0.109; HD IVUS to OCT: FS=0.826 vs 0.125; OCT to IVUS: FS=0.782 vs 0.068; IVUS to OCT: FS=0.815 vs 0.123), and improved performance in semi-supervised learning, especially when only limited labeled data was available.

CONCLUSION

The intravascular images of OCT and IVUS can provide reciprocal assistance to each other in stent 3D reconstruction by cross-modal translation, where the stent consistency in synthetic images was maintained by disentangled representation.

Advances in Diagnosis, Therapy, and Prognosis of Coronary Artery Disease Powered by Deep Learning Algorithms

Percutaneous coronary intervention has been a standard treatment strategy for patients with coronary artery disease with continuous ebullient progress in technology and techniques. The application of artificial intelligence and deep learning in particular is currently boosting the development of interventional solutions, improving the efficiency and objectivity of diagnosis and treatment. The ever-growing amount of data and computing power together with cutting-edge algorithms pave the way for the integration of deep learning into clinical practice, which has revolutionized the interventional workflow in imaging processing, interpretation, and navigation. This review discusses the development of deep learning algorithms and their corresponding evaluation metrics together with their clinical applications. Advanced deep learning algorithms create new opportunities for precise diagnosis and tailored treatment with a high degree of automation, reduced radiation, and enhanced risk stratification. Generalization, interpretability, and regulatory issues are remaining challenges that need to be addressed through joint efforts from multidisciplinary community.

OCTOPUS - Optical coherence tomography plaque and stent analysis software

Background and objective

Compared with other imaging modalities, intravascular optical coherence tomography (IVOCT) has significant advantages for guiding percutaneous coronary interventions, assessing their outcomes, and characterizing plaque components. To aid IVOCT research studies, we developed the Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) analysis software, which provides highly automated, comprehensive analysis of coronary plaques and stents in IVOCT images.

Methods

User specifications for OCTOPUS were obtained from detailed, iterative discussions with IVOCT analysts in the Cardiovascular Imaging Core Laboratory at University Hospitals Cleveland Medical Center, a leading laboratory for IVOCT image analysis. To automate image analysis results, the software includes several important algorithmic steps: pre-processing, deep learning plaque segmentation, machine learning identification of stent struts, and registration of pullbacks for sequential comparisons. Intuitive, interactive visualization and manual editing of segmentations were included in the software. Quantifications include stent deployment characteristics (e.g., stent area and stent strut malapposition), strut level analysis, calcium angle, and calcium thickness measurements. Interactive visualizations include (x,y) anatomical, en face, and longitudinal views with optional overlays (e.g., segmented calcifications). To compare images over time, linked visualizations were enabled to display up to four registered vessel segments at a time.

Results

OCTOPUS has been deployed for nearly 1 year and is currently being used in multiple IVOCT studies. Underlying plaque segmentation algorithm yielded excellent pixel-wise results (86.2% sensitivity and 0.781 F1 score). Using OCTOPUS on 34 new pullbacks, we determined that following automated segmentation, only 13% and 23% of frames needed any manual touch up for detailed lumen and calcification labeling, respectively. Only up to 3.8% of plaque pixels were modified, leading to an average editing time of only 7.5 s/frame, an approximately 80% reduction compared to manual analysis. Regarding stent analysis, sensitivity and precision were both greater than 90%, and each strut was successfully classified as either covered or uncovered with high sensitivity (94%) and specificity (90%). We demonstrated use cases for sequential analysis. To analyze plaque progression, we loaded multiple pullbacks acquired at different points (e.g., pre-stent, 3-month follow-up, and 18-month follow-up) and evaluated frame-level development of in-stent neo-atherosclerosis. In ex vivo cadaver experiments, the OCTOPUS software enabled visualization and quantitative evaluation of irregular stent deployment in the presence of calcifications identified in pre-stent images.

Conclusions

We introduced and evaluated the clinical application of a highly automated software package, OCTOPUS, for quantitative plaque and stent analysis in IVOCT images. The software is currently used as an offline tool for research purposes; however, the software's embedded algorithms may also be useful for real-time treatment planning.

Towards a Deep-Learning Approach for Prediction of Fractional Flow Reserve from Optical Coherence Tomography

Cardiovascular disease (CVD) is the number one cause of death worldwide, and coronary artery disease (CAD) is the most prevalent CVD, accounting for 42% of these deaths. In view of the limitations of the anatomical evaluation of CAD, Fractional Flow Reserve (FFR) has been introduced as a functional diagnostic index. Herein, we evaluate the feasibility of using deep neural networks (DNN) in an ensemble approach to predict the invasively measured FFR from raw anatomical information that is extracted from optical coherence tomography (OCT). We evaluate the performance of various DNN architectures under different formulations: regression, classification—standard, and few-shot learning (FSL) on a dataset containing 102 intermediate lesions from 80 patients. The FSL approach that is based on a convolutional neural network leads to slightly better results compared to the standard classification: the per-lesion accuracy, sensitivity, and specificity were 77.5%, 72.9%, and 81.5%, respectively. However, since the 95% confidence intervals overlap, the differences are statistically not significant. The main findings of this study can be summarized as follows: (1) Deep-learning (DL)-based FFR prediction from reduced-order raw anatomical data is feasible in intermediate coronary artery lesions; (2) DL-based FFR prediction provides superior diagnostic performance compared to baseline approaches that are based on minimal lumen diameter and percentage diameter stenosis; and (3) the FFR prediction performance increases quasi-linearly with the dataset size, indicating that a larger train dataset will likely lead to superior diagnostic performance.

Structure attention co-training neural network for neovascularization segmentation in intravascular optical coherence tomography

PURPOSE

To development and validate a Neovascularization (NV) segmentation model in intravascular optical coherence tomography (IVOCT) through deep learning methods.

METHODS AND MATERIALS

A total of 1950 2D slices of 70 IVOCT pullbacks were used in our study. We randomly selected 1273 2D slices from 44 patients as the training set, 379 2D slices from 11 patients as the validation set, and 298 2D slices from the last 15 patients as the testing set. Automatic NV segmentation is quite challenging, as it must address issues of speckle noise, shadow artifacts, high distribution variation, etc. To meet these challenges, a new deep learning-based segmentation method is developed based on a co-training architecture with an integrated structural attention mechanism. Co-training is developed to exploit the features of three consecutive slices. The structural attention mechanism comprises spatial and channel attention modules and is integrated into the co-training architecture at each up-sampling step. A cascaded fixed network is further incorporated to achieve segmentation at the image level in a coarse-to-fine manner.

RESULTS

Extensive experiments were performed involving a comparison with several state-of-the-art deep learning-based segmentation methods. Moreover, the consistency of the results with those of manual segmentation was also investigated. Our proposed NV automatic segmentation method achieved the highest correlation with the manual delineation by interventional cardiologists (the Pearson correlation coefficient is 0.825).

CONCLUSION

In this work, we proposed a co-training architecture with an integrated structural attention mechanism to segment NV in IVOCT images. The good agreement between our segmentation results and manual segmentation indicates that the proposed method has great potential for application in the clinical investigation of NV-related plaque diagnosis and treatment. This article is protected by copyright. All rights reserved.

Automated segmentation of metal stent and bioresorbable vascular scaffold in intravascular optical coherence tomography images using deep learning architectures

Percutaneous coronary intervention (PCI) with stent placement is a treatment effective for coronary artery diseases. Intravascular optical coherence tomography (OCT) with high resolution is used clinically to visualize stent deployment and restenosis, facilitating PCI operation and for complication inspection. Automated stent struts segmentation in OCT images is necessary as each pullback of OCT images could contain thousands of stent struts. In this paper, a deep learning framework is proposed and demonstrated for the automated segmentation of two major clinical stent types: metal stents and bioresorbable vascular scaffolds (BVS). U-Net, the current most prominent deep learning network in biomedical segmentation, was implemented for segmentation with cropped input. The architectures of MobileNetV2 and DenseNet121 were also adapted into U-Net for improvement in speed and accuracy. The results suggested that the proposed automated algorithm's segmentation performance approaches the level of independent human obsevers and is feasible for both types of stents despite their distinct appearance. U-Net with DenseNet121 encoder (U-Dense) performed best with Dice's coefficient of 0.86 for BVS segmentation, and precision/recall of 0.92/0.92 for metal stent segmentation under optimal crop window size of 256.

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.

A comprehensive scoping review of Bayesian networks in healthcare: Past, present and future

No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the future. This unique and novel scoping review of BNs in healthcare provides an analytical framework for comprehensively characterizing the domain and its current state. A literature search of health and health informatics literature databases using relevant keywords found 3810 articles that were reduced to 123. This was after screening out those presenting Bayesian statistics, meta-analysis or neural networks, as opposed to BNs and those describing the predictive performance of multiple machine learning algorithms, of which BNs were simply one type. Using the novel analytical framework, we show that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exist in the way BNs in healthcare are presented in the literature, which impacts understanding, consensus towards systematic methodologies, practice and adoption; and (4) a gap exists between having an accurate BN and a useful BN that impacts clinical practice. This review highlights several neglected issues, such as restricted aims of BNs, ad hoc BN development methods, and the lack of BN adoption in practice and reveals to researchers and clinicians the need to address these problems. To map the way forward, the paper proposes future research directions and makes recommendations regarding BN development methods and adoption in practice.

Bayesian networks in healthcare: What is preventing their adoption?

There has been much research effort expended toward the use of Bayesian networks (BNs) in medical decision-making. However, because of the gap between developing an accurate BN and demonstrating its clinical usefulness, this has not resulted in any widespread BN adoption in clinical practice. This paper investigates this problem with the aim of finding an explanation and ways to address the problem through a comprehensive literature review of articles describing BNs in healthcare. Based on the literature collection that has been systematically narrowed down from 3810 to 116 most relevant articles, this paper analyses the benefits, barriers and facilitating factors (BBF) for implementing BN-based systems in healthcare using the ITPOSMO-BBF framework. A key finding is that works in the literature rarely consider barriers and even when these were identified they were not connected to facilitating factors. The main finding is that the barriers can be grouped into: (1) data inadequacies; (2) clinicians' resistance to new technologies; (3) lack of clinical credibility; (4) failure to demonstrate clinical impact; (5) absence of an acceptable predictive performance; and (6) absence of evidence for model's generalisability. The facilitating factors can be grouped into: (1) data collection improvements; (2) software and technological improvements; (3) having interpretable and easy to use BN-based systems; (4) clinical involvement in the development or review of the model; (5) investigation of model's clinical impact; (6) internal validation of the model's performance; and (7) external validation of the model. These groupings form a strong basis for a generic framework that could be used for formulating strategies for ensuring BN-based clinical decision-support system adoption in frontline care settings. The output of this review is expected to enhance the dialogue among researchers by providing a deeper understanding for the neglected issue of BN adoption in practice and promoting efforts for implementing BN-based systems.

Automatic Detection of Coronary Metallic Stent Struts Based on YOLOv3 and R-FCN

An artificial stent implantation is one of the most effective ways to treat coronary artery diseases. It is vital in vascular medical imaging, such as intravascular optical coherence tomography (IVOCT), to be able to track the position of stents in blood vessels effectively. We trained two models, the “You Only Look Once” version 3 (YOLOv3) and the Region-based Fully Convolutional Network (R-FCN), to detect metal support struts in IVOCT, respectively. After rotating the original images in the training set for data augmentation, and modifying the scale of the conventional anchor box in both two algorithms to fit the size of the target strut, YOLOv3 and R-FCN achieved precision, recall, and AP all above 95% in 0.4 IoU threshold. And R-FCN performs better than YOLOv3 in all relevant indicators.

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.

Patient-Specific Coronary Artery 3D Printing Based on Intravascular Optical Coherence Tomography and Coronary Angiography

Despite the new ideas were inspired in medical treatment by the rapid advancement of three-dimensional (3D) printing technology, there is still rare research work reported on 3D printing of coronary arteries being documented in the literature. In this work, the application value of 3D printing technology in the treatment of cardiovascular diseases has been explored via comparison study between the 3D printed vascular solid model and the computer aided design (CAD) model. In this paper, a new framework is proposed to achieve a 3D printing vascular model with high simulation. The patient-specific 3D reconstruction of the coronary arteries is performed by the detailed morphological information abstracted from the contour of the vessel lumen. In the process of reconstruction which has 5 steps, the morphological details of the contour view of the vessel lumen are merged along with the curvature and length information provided by the coronary angiography. After comparing with the diameter of the narrow section and the diameter of the normal section in CAD models and 3D printing model, it can be concluded that there is a high correlation between the diameter of vascular stenosis measured in 3D printing models and computer aided design models. The 3D printing model has high-modeling ability and high precision, which can represent the original coronary artery appearance accurately. It can be adapted for prevascularization planning to support doctors in determining the surgical procedures.

Automatic Annotation of Retinal Layers in Optical Coherence Tomography Images

Early diagnosis of retinal OCT images has been shown to curtail blindness and visual impairments. However, the advancement of ophthalmic imaging technologies produces an ever-growing scale of retina images, both in volume and variety, which overwhelms the ophthalmologist ability to segment these images. While many automated methods exist, speckle noise and intensity inhomogeneity negatively impacts the performance of these methods. We present a comprehensive and fully automatic method for annotation of retinal layers in OCT images comprising of fuzzy histogram hyperbolisation (FHH) and graph cut methods to segment 7 retinal layers across 8 boundaries. The FHH handles speckle noise and inhomogeneity in the preprocessing step. Then the normalised vertical image gradient, and it’s inverse to represent image intensity in calculating two adjacency matrices and then the FHH reassigns the edge-weights to make edges along retinal boundaries have a low cost, and graph cut method identifies the shortest-paths (layer boundaries). The method is evaluated on 150 B-Scan images, 50 each from the temporal, foveal and nasal regions were used in our study. Promising experimental results have been achieved with high tolerance and adaptability to contour variance and pathological inconsistency of the retinal layers in all (temporal, foveal and nasal) regions. The method also achieves high accuracy, sensitivity, and Dice score of 0.98360, 0.9692 and 0.9712, respectively in segmenting the retinal nerve fibre layer. The annotation can facilitate eye examination by providing accurate results. The integration of the vertical gradients into the graph cut framework, which captures the unique characteristics of retinal structures, is particularly useful in finding the actual minimum paths across multiple retinal layer boundaries. Prior knowledge plays an integral role in image segmentation.

Automated stent coverage analysis in intravascular OCT (IVOCT) image volumes using a support vector machine and mesh growing

Absence of vascular-stent tissue coverage by IVOCT is a biomarker for potential stent-related thrombosis. We developed highly-automated algorithms to classify covered and uncovered struts and quantitatively evaluate stent apposition. We trained a machine learning model on 7,125 images, and included an active learning, relabeling step to improve noisy labels. We obtained uncovered strut classification sensitivity/specificity (94%/90%) comparable to analyst inter-and-intra-observer variability and AUC (0.97), and tissue coverage thickness measurement arguably better than the commercial product. By comparing classification models from regular and relabeled data sets, we observed robustness of the support vector machine to noisy data. A graph-based algorithm detected clusters of uncovered struts thought to pose a greater risk than isolated uncovered struts. The software enables highly-automated, objective, repeatable, comprehensive stent analysis.

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.

Corner Detection Based Automatic Segmentation of Bioresorbable Vascular Scaffold Struts in IVOCT Images

Bioresorbable Vascular scaffold (BVS) is a promising type of stent in percutaneous coronary intervention. Struts apposition assessment is important to ensure the safety of implanted BVS. Currently, BVS struts apposition analysis in IVOCT images still depends on manual delineation of struts, which is labor intensive and time consuming. Automatic struts segmentation is highly desired to simplify and speed up quantitative analysis. However, it is difficult to segment struts accurately based on the contour, due to the influence of fractures inside strut and blood artifacts around strut. In this paper, a novel framework of automatic struts segmentation based on four corners is introduced, in which priori knowledge is utilized that struts have obvious feature of box-shape. Firstly, a cascaded AdaBoost classifier based on enriched haar-like features is trained to detect struts corners. Then, segmentation result can be obtained based on the four detected corners of each strut. Tested on five pullbacks consisting of 483 images with strut, our novel method achieved an average Dice's coefficient of 0.82 for strut segmentation areas. It concludes that our method can segment struts accurately and robustly. Furthermore, automatic struts malapposition analysis in clinical practice is feasible based on the segmentation results.

Assessment of intracoronary stent location and extension in intravascular ultrasound sequences

PURPOSE

An intraluminal coronary stent is a metal scaffold deployed in a stenotic artery during percutaneous coronary intervention (PCI). In order to have an effective deployment, a stent should be optimally placed with regard to anatomical structures such as bifurcations and stenoses. Intravascular ultrasound (IVUS) is a catheter-based imaging technique generally used for PCI guiding and assessing the correct placement of the stent. A novel approach that automatically detects the boundaries and the position of the stent along the IVUS pullback is presented. Such a technique aims at optimizing the stent deployment.

METHODS

The method requires the identification of the stable frames of the sequence and the reliable detection of stent struts. Using these data, a measure of likelihood for a frame to contain a stent is computed. Then, a robust binary representation of the presence of the stent in the pullback is obtained applying an iterative and multiscale quantization of the signal to symbols using the Symbolic Aggregate approXimation algorithm.

RESULTS

The technique was extensively validated on a set of 103 IVUS of sequences of in vivo coronary arteries containing metallic and bioabsorbable stents acquired through an international multicentric collaboration across five clinical centers. The method was able to detect the stent position with an overall F-measure of 86.4%, a Jaccard index score of 75% and a mean distance of 2.5 mm from manually annotated stent boundaries, and in bioabsorbable stents with an overall F-measure of 88.6%, a Jaccard score of 77.7 and a mean distance of 1.5 mm from manually annotated stent boundaries. Additionally, a map indicating the distance between the lumen and the stent along the pullback is created in order to show the angular sectors of the sequence in which the malapposition is present.

CONCLUSIONS

Results obtained comparing the automatic results vs the manual annotation of two observers shows that the method approaches the interobserver variability. Similar performances are obtained on both metallic and bioabsorbable stents, showing the flexibility and robustness of the method.

Histogram-based standardization of intravascular optical coherence tomography images acquired from different imaging systems

PURPOSE

Intravascular optical coherence tomography (OCT) is widely used for analysis of the coronary artery disease. Its high spatial resolution allows for visualization of arterial tissue components in detail. There are different OCT systems on the market, each of which produces data characterized by its own intensity range and distribution. These differences should be taken into account for the development of image processing algorithms. In order to overcome this difference in the intensity range and distribution, we developed a framework for matching intensities based on the exact histogram matching technique.

METHODS

In our method, the key step for using the exact histogram matching is to determine the target histogram. For this, we proposed two schemes: a global scheme that uses a single histogram as the target histogram for all the pullbacks, and a local scheme that selects for each single image a target histogram from a predefined database. These two schemes are compared on a unique dataset containing pairs of pullbacks that were acquired shortly after each other with systems from two vendors, St. Jude and Terumo. Pullbacks were aligned according to anatomical landmarks, and a database of matched histogram pairs was created. A leave-one-out cross validation was used to compare performance of the two schemes. The matching accuracy was evaluated by comparing: (a) histograms using Euclidean (dx2 ) and Kolmogorov-Smirnov (dKS ) distances, and (b) median intensity level within anatomical regions of interest.

RESULTS

Leave-one-out validation indicated that both matching schemes yield comparably high accuracies across the entire validation dataset. The local scheme outperforms the global scheme with marginally lower dissimilarities at both histogram level and intensity level. High visual similarity was observed when comparing the matched images to their aligned counterparts.

CONCLUSION

Both local and global schemes are robust and produce accurate intensity matching. While local scheme performs marginally better than the global scheme, it requires a predefined histogram dataset and is more time consuming. Thus, for offline standardization of the images, the local scheme should be preferred for being more accurate. For online standardization or when another system is involved, the global scheme can be used as a simple and nearly-as-accurate alternative.

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.

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%.

Circumferential optical coherence tomography angiography imaging of the swine esophagus using a micromotor balloon catheter

We demonstrate a micromotor balloon imaging catheter for ultrahigh speed endoscopic optical coherence tomography (OCT) which provides wide area, circumferential structural and angiographic imaging of the esophagus without contrast agents. Using a 1310 nm MEMS tunable wavelength swept VCSEL light source, the system has a 1.2 MHz A-scan rate and ~8.5 µm axial resolution in tissue. The micromotor balloon catheter enables circumferential imaging of the esophagus at 240 frames per second (fps) with a ~30 µm (FWHM) spot size. Volumetric imaging is achieved by proximal pullback of the micromotor assembly within the balloon at 1.5 mm/sec. Volumetric data consisting of 4200 circumferential images of 5,000 A-scans each over a 2.6 cm length, covering a ~13 cm(2) area is acquired in <18 seconds. A non-rigid image registration algorithm is used to suppress motion artifacts from non-uniform rotational distortion (NURD), cardiac motion or respiration. En face OCT images at various depths can be generated. OCT angiography (OCTA) is computed using intensity decorrelation between sequential pairs of circumferential scans and enables three-dimensional visualization of vasculature. Wide area volumetric OCT and OCTA imaging of the swine esophagus in vivo is demonstrated.

Quantitative analysis of the side-branch orifice after bifurcation stenting using en-face processing of OCT images: a comparison between Xience V and Resolute Integrity stents

OBJECTIVE

Methods for intravascular assessment of the side-branch (SB) orifice after stenting are not readily available. The aim of this study was to assess the utility of an en-face projection processing for optical coherence tomography (OCT) images for SB evaluation.

METHODS

Measurements of the SB orifice obtained using en-face OCT images were validated using a phantom model. Linear regression modeling was applied to estimated area measurements made on the en-face images. The SB orifice was then analyzed in 88 patients with bifurcation lesions treated with either Xience V (everolimus-eluting stent) or Resolute Integrity [zotarolimus-eluting stent (ZES)]. The SB orifice area (A) and the area obstructed by struts (B) were calculated, and the %open area was evaluated as (A-B)/A*100.

RESULTS

Linear regression modeling demonstrated that the observed departures of the intercept and slope were not significantly different from 0 (-0.12 ± 0.22, P=0.59) and 1 (1.01 ± 0.06, R(2)=0.88, P=0.87), respectively. In cases without SB dilatation, the %open area was significantly larger in the everolimus-eluting stent group (n=25) than in the ZES group [n=32; 89.2% (83.7-91.3) vs. 84.3% (78.9-87.8), P=0.04]. A significant difference in %open area between cases with and those without SB dilatation was demonstrated in the ZES group [91.4% (86.1-94.0) vs. 84.3% (78.9-87.8), P=0.04].

CONCLUSION

The accuracy of SB orifice measurement on an en-face OCT image was validated using a phantom model. This novel approach enables quantitative evaluation of the differences in SB orifice area free from struts among different stent types and different treatment strategies in vivo.