Automatic detection of bioresorbable vascular scaffold struts in intravascular optical coherence tomography pullback runs

Bioresorbable vascular scaffolds (BVS) have gained significant interest in both the technical and clinical communities as a possible alternative to metallic stents. For accurate BVS analysis, intravascular optical coherence tomography (IVOCT) is currently the most suitable imaging technique due to its high resolution and the translucency of polymeric BVS struts for near infrared light. However, given the large number of struts in an IVOCT pullback run, quantitative analysis is only feasible when struts are detected automatically. In this paper, we present an automated method to detect and measure BVS struts based on their black cores in IVOCT images. Validated using 3 baseline and 3 follow-up data sets, the method detected 93.7% of 4691 BVS struts correctly with 1.8% false positives. In total, the Dice’s coefficient for BVS strut areas was 0.84. It concludes that this method can detect BVS struts accurately and robustly for tissue coverage measurement, malapposition detection, strut distribution analysis or 3D scaffold reconstruction.OCIS codes: (100.6950) Tomographic image processing, (110.4500) Optical coherence tomography     

Fully automatic three-dimensional visualization of intravascular optical coherence tomography images: methods and feasibility in vivo

Intravascular optical coherence tomography (IV-OCT) is an imaging modality that can be used for the assessment of intracoronary stents. Recent publications pointed to the fact that 3D visualizations have potential advantages compared to conventional 2D representations. However, 3D imaging still requires a time consuming manual procedure not suitable for on-line application during coronary interventions. We propose an algorithm for a rapid and fully automatic 3D visualization of IV-OCT pullbacks. IV-OCT images are first processed for the segmentation of the different structures. This also allows for automatic pullback calibration. Then, according to the segmentation results, different structures are depicted with different colors to visualize the vessel wall, the stent and the guide-wire in details. Final 3D rendering results are obtained through the use of a commercial 3D DICOM viewer. Manual analysis was used as ground-truth for the validation of the segmentation algorithms. A correlation value of 0.99 and good limits of agreement (Bland Altman statistics) were found over 250 images randomly extracted from 25 in vivo pullbacks. Moreover, 3D rendering was compared to angiography, pictures of deployed stents made available by the manufacturers and to conventional 2D imaging corroborating visualization results. Computational time for the visualization of an entire data sets resulted to be ~74 sec. The proposed method allows for the on-line use of 3D IV-OCT during percutaneous coronary interventions, potentially allowing treatments optimization.OCIS codes: (170.4500) Optical coherence tomography, (100.6890) Three-dimensional image processing, (330.5000) Vision - patterns and recognition     

Stent implant follow-up in intravascular optical coherence tomography images

The objectives of this article are (i) to utilize computer methods in detection of stent struts imaged in vivo by optical coherence tomography (OCT) during percutaneous coronary interventions (PCI); (ii) to provide measurements for the assessment and monitoring of in-stent restenosis by OCT post PCI. Thirty-nine OCT cross-sections from seven pullbacks from seven patients presenting varying degrees of neointimal hyperplasia (NIH) are selected, and stent struts are detected. Stent and lumen boundaries are reconstructed and one experienced observer analyzed the strut detection, the lumen and stent area measurements, as well as the NIH thickness in comparison to manual tracing using the reviewing software provided by the OCT manufacturer (LightLab Imaging, MA, USA). Very good agreements were found between the computer methods and the expert evaluations for lumen cross-section area (mean difference = 0.11 ± 0.70 mm²; r ² = 0.98, P < 0.0001) and the stent cross-section area (mean difference = 0.10 ± 1.28 mm²; r ² = 0.85, P value <  0.0001). The average number of detected struts was 10.4 ± 2.9 per cross-section when the expert identified 10.5 ± 2.8 (r ² = 0.78, P value < 0.0001). For the given patient dataset: lumen cross-sectional area was on the average (6.05 ± 1.87 mm²), stent cross-sectional area was (6.26 ± 1.63 mm²), maximum angle between struts was on the average (85.96 ± 54.23°), maximum, average, and minimum distance between the stent and the lumen were (0.18 ± 0.13 mm), (0.08 ± 0.06 mm), and (0.01 ± 0.02 mm), respectively, and stent eccentricity was (0.80 ± 0.08). Low variability between the expert and automatic method was observed in the computations of the most important parameters assessing the degree of neointimal tissue growth in stents imaged by OCT pullbacks. After further extensive validation, the presented methods might offer a robust automated tool that will improve the evaluation and follow-up monitoring of in-stent restenosis in patients.     

In vivo detection of plaque erosion by intravascular optical coherence tomography using artificial intelligence

Plaque erosion is one of the most common underlying mechanisms for acute coronary syndrome (ACS). Optical coherence tomography (OCT) allows in vivo diagnosis of plaque erosion. However, challenge remains due to high inter- and intra-observer variability. We developed an artificial intelligence method based on deep learning for fully automated detection of plaque erosion in vivo, which achieved a recall of 0.800 ± 0.175, a precision of 0.734 ± 0.254, and an area under the precision-recall curve (AUC) of 0.707. Our proposed method is in good agreement with physicians, and can help improve the clinical diagnosis of plaque erosion and develop individualized treatment strategies for optimal management of ACS patients.     

Volumetric quantification of fibrous caps using intravascular optical coherence tomography

The rupture of thin-cap fibroatheroma accounts for most acute coronary events. Optical Coherence Tomography (OCT) allows quantification of fibrous cap (FC) thickness in vivo. Conventional manual analysis, by visually determining the thinnest part of the FC is subject to inter-observer variability and does not capture the 3-D morphology of the FC. We propose and validate a computer-aided method that allows volumetric analysis of FC. The radial FC boundary is semi-automatically segmented using a dynamic programming algorithm. The thickness at every point of the FC boundary, along with 3-D morphology of the FC, can be quantified. The method was validated against three experienced OCT image analysts in 14 lipid-rich lesions. The proposed method may advance our understanding of the mechanisms behind plaque rupture and improve disease management.     

Artery phantoms for intravascular optical coherence tomography: healthy arteries

We present a method to make phantoms of coronary arteries for intravascular optical coherence tomography (IV-OCT). The phantoms provide a calibrated OCT response similar to the layered structure of arteries. The optical properties of each layer are achieved with specific concentrations of alumina and carbon black in a silicone matrix. This composition insures high durability and also approximates the elastic properties of arteries. The phantoms are fabricated in a tubular shape by the successive deposition and curing of liquid silicone mixtures on a lathe setup.     

Method for parametric imaging of attenuation by intravascular optical coherence tomography

Catheter-based intravascular optical coherence tomography (IVOCT) is a powerful imaging modality for visualization of atherosclerosis with high resolution. Quantitative characterization of various tissue types by attenuation coefficient (AC) extraction has been proven to be a potentially significant application of OCT attenuation imaging. However, existing methods for AC extraction from OCT suffer from the challenge of variability in complex tissue types in IVOCT pullback data such as healthy vessel wall, mixed atherosclerotic plaques, plaques with a single component and stent struts, etc. This challenge leads to the ineffectiveness in the tissue differentiation by AC representation based on single scattering model of OCT signal. In this paper, we propose a novel method based on multiple scattering model for parametric imaging of optical attenuation by AC retrieval from IVOCT images conventionally acquired during cardiac catheterization. The OCT signal characterized by the AC is physically modeled by Monte Carlo simulation. Then, the pixel-wise AC retrieval is achieved by iteratively minimizing an error function regarding the modeled and measured backscattered signal. This method provides a general scheme for AC extraction from IVOCT without the premise of complete attenuation of the incident light through the imaging depths. Results of computer-simulated and clinical images demonstrate that the method can avoid overestimation at the end of the depth profile in comparison with the approaches based on the depth-resolved (DR) model. The energy error depth and structural similarity are improved by about 30% and 10% respectively compared with DR. It provides a useful way to differentiate and characterize arterial tissue types in IVOCT images.     

编织冠状动脉的血管内超声及光学相干断层显像影像特点

目的:总结编织冠状动脉(WCA)的血管内超声(IVUS)及光学相干断层显像(OCT)影像特点。方法:选取2013年1月至2020年7月于成都中医药大学附属医院、复旦大学附属中山医院、郑州市心血管病医院就诊,且冠状动脉造影疑诊WCA的患者37例,进行腔内影像学分析,总结确诊为WCA患者的IVUS及OCT特点。结果:37例...     

Doppler velocity detection limitations in spectrometer-based versus swept-source optical coherence tomography

Recent advances in Doppler techniques have enabled high sensitivity imaging of biological flow to measure blood velocities and vascular perfusion. Here we compare spectrometer-based and wavelength-swept Doppler OCT implementations theoretically and experimentally, characterizing the lower and upper observable velocity limits in each configuration. We specifically characterize the washout limit for Doppler OCT, the velocity at which signal degradation results in loss of flow information, which is valid for both quantitative and qualitative flow imaging techniques. We also clearly differentiate the washout effect from the separate phenomenon of phase wrapping. We demonstrate that the maximum detectable Doppler velocity is determined by the fringe washout limit and not phase wrapping. Both theory and experimental results from phantom flow data and retinal blood flow data demonstrate the superiority of the swept-source technique for imaging vessels with high flow rates.     

Depth resolved detection of lipid using spectroscopic optical coherence tomography

Optical frequency domain imaging (OFDI) can identify key components related to plaque vulnerability but can suffer from artifacts that could prevent accurate identification of lipid rich regions. In this paper, we present a model of depth resolved spectral analysis of OFDI data for improved detection of lipid. A quadratic Discriminant analysis model was developed based on phantom compositions known chemical mixtures and applied to a tissue phantom of a lipid-rich plaque. We demonstrate that a combined spectral and attenuation model can be used to predict the presence of lipid in OFDI images.OCIS codes: (110.4500) Optical coherence tomography, (170.1580) Chemometrics, (170.6510) Spectroscopy, tissue diagnostics     

Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography

Optical coherence tomography angiography has recently been used to visualize choroidal neovascularization (CNV) in participants with age-related macular degeneration. Identification and quantification of CNV area is important clinically for disease assessment. An automated algorithm for CNV area detection is presented in this article. It relies on denoising and a saliency detection model to overcome issues such as projection artifacts and the heterogeneity of CNV. Qualitative and quantitative evaluations were performed on scans of 7 participants. Results from the algorithm agreed well with manual delineation of CNV area.OCIS codes: (170.4500) Optical coherence tomography, (170.3880) Medical and biological imaging, (100.0100) Image processing     

Morphological analysis of optical coherence tomography images for automated classification of gastrointestinal tissues

The impact of digestive diseases, which include disorders affecting the oropharynx and alimentary canal, ranges from the inconvenience of a transient diarrhoea to dreaded conditions such as pancreatic cancer, which are usually fatal. Currently, the major limitation for the diagnosis of such diseases is sampling error because, even in the cases of rigorous adherence to biopsy protocols, only a tiny fraction of the surface of the involved gastrointestinal tract is sampled. Optical coherence tomography (OCT), which is an interferometric imaging technique for the minimally invasive measurement of biological samples, could decrease sampling error, increase yield, and even eliminate the need for tissue sampling provided that an automated, quick and reproducible tissue classification system is developed. Segmentation and quantification of ophthalmologic pathologies using OCT traditionally rely on the extraction of thickness and size measures from the OCT images, but layers are often not observed in nonopthalmic OCT imaging. Distinct mathematical methods, namely Principal Component Analysis (PCA) and textural analyses including both spatial textural analysis derived from the two-dimensional discrete Fourier transform (DFT) and statistical texture analysis obtained independently from center-symmetric autocorrelation (CSAC) and spatial grey-level dependency matrices (SGLDM), have been previously reported to overcome this problem. We propose an alternative approach consisting of a region segmentation according to the intensity variation along the vertical axis and a pure statistical technique for feature quantification, i.e. morphological analysis. Qualitative and quantitative comparisons with traditional approaches are accomplished in the discrimination of freshly-excised specimens of gastrointestinal tissues to exhibit the feasibility of the proposed method for computer-aided diagnosis (CAD) in the clinical setting.     

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

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

Interstudy reproducibility of the second generation, Fourier domain optical coherence tomography in patients with coronary artery disease and comparison with intravascular ultrasound: a study applying automated contour detection

Recently, Fourier domain OCT (FD-OCT) has been introduced for clinical use. This approach allows in vivo, high resolution (15 micron) imaging with very fast data acquisition, however, it requires brief flushing of the lumen during imaging. The reproducibility of such fast data acquisition under intracoronary flush application is poorly understood. To assess the inter-study variability of FD-OCT and to compare lumen morphometry to the established invasive imaging method, IVUS. 18 consecutive patients with coronary artery disease scheduled for PCI were included. In each target vessel a FD-OCT pullback (MGH system, light source 1,310 nm, 105 fps, pullback speed 20 mm/s) was acquired during brief (3 s) injection of X-ray contrast (flow 3 ml/s) through the guiding catheter. A second pullback was repeated under the same conditions after re-introduction of the FD OCT catheter into the coronary artery. IVUS and OCT imaging was performed in random order. FD-OCT and IVUS pullback data were analyzed using a recently developed software employing semi automated lumen contour and stent strut detection algorithms. Corresponding ROI were matched based on anatomical landmarks such as side branches and/or stent edges. Inter-study variability is presented as the absolute difference between the two pullbacks. FD-OCT showed remarkably good reproducibility. Inter-study variability in native vessels (cohort A) was very low for mean and minimal luminal area (0.10 ± 0.38, 0.19 ± 0.57 mm², respectively). Likewise inter-study variability was very low in stented coronary segments (cohort B) for mean lumen, mean stent, minimal luminal and minimal stent area (0.06 ± 0.08, 0.07 ± 0.10, 0.04 ± 0.09, 0.04 ± 0.10 mm², respectively). Comparison to IVUS morphometry revealed no significant differences. The differences between both imaging methods, OCT and IVUS, were very low for mean lumen, mean stent, minimal luminal and minimal stent area (0.10 ± 0.45, 0.10 ± 0.36, 0.26 ± 0.54, 0.05 ± 0.47 mm², respectively). FD-OCT shows excellent reproducibility and very low inter-study variability in both, native and stented coronary segments. No significant differences in quantitative lumen morphometry were observed between FD-OCT and IVUS. Evaluating these results suggest that FD-OCT is a reliable imaging tool to apply in longitudinal coronary artery disease studies.     

Volumetric directional optical coherence tomography

Photoreceptor loss and resultant thinning of the outer nuclear layer (ONL) is an important pathological feature of retinal degenerations and may serve as a useful imaging biomarker for age-related macular degeneration. However, the demarcation between the ONL and the adjacent Henle’s fiber layer (HFL) is difficult to visualize with standard optical coherence tomography (OCT). A dedicated OCT system that can precisely control and continuously and synchronously update the imaging beam entry points during scanning has not been realized yet. In this paper, we introduce a novel imaging technology, Volumetric Directional OCT (VD-OCT), which can dynamically adjust the incident beam on the pupil without manual adjustment during a volumetric OCT scan. We also implement a customized spoke-circular scanning pattern to observe the appearance of HFL with sufficient optical contrast in continuous cross-sectional scans through the entire volume. The application of VD-OCT for retinal imaging to exploit directional reflectivity properties of tissue layers has the potential to allow for early identification of retinal diseases.     

Heartbeat OCT: in vivo intravascular megahertz-optical coherence tomography

Cardiac motion artifacts, non-uniform rotational distortion and undersampling affect the image quality and the diagnostic impact of intravascular optical coherence tomography (IV-OCT). In this study we demonstrate how these limitations of IV-OCT can be addressed by using an imaging system that we called “Heartbeat OCT”, combining a fast Fourier Domain Mode Locked laser, fast pullback, and a micromotor actuated catheter, designed to examine a coronary vessel in less than one cardiac cycle. We acquired in vivo data sets of two coronary arteries in a porcine heart with both Heartbeat OCT, working at 2.88 MHz A-line rate, 4000 frames/s and 100 mm/s pullback speed, and with a commercial system. The in vivo results show that Heartbeat OCT provides faithfully rendered, motion-artifact free, fully sampled vessel wall architecture, unlike the conventional IV-OCT data. We present the Heartbeat OCT system in full technical detail and discuss the steps needed for clinical translation of the technology.OCIS codes: (170.4500) Optical coherence tomography, (170.3880) Medical and biological imaging, (170.2150) Endoscopic imaging, (110.6880) Three-dimensional image acquisition, (110.2350) Fiber optics imaging, (120.5800) Scanners, (120.3890) Medical optics instrumentation     

Developing cross-correlation as a method for microvessel imaging using clinical intravascular optical coherence tomography systems

Current clinical intravascular optical coherence tomography (IV-OCT) imaging systems have limited in-vivo flow imaging capability because of non-uniform catheter rotation and inadequate A-line scan density. Thus any flow-localisation method that seeks to identify sites of variation within the OCT image data-sets, whether that is in amplitude or phase, produces non-representative correlation (or variance) maps. In this study, both mean and the variation within a set of cross-correlation maps, for static OCT imaging was used to differentiate flow from nonflow regions. Variation was quantified by use of standard deviation. The advantage of this approach is its ability to image flow, even in the presence of motion artifacts. The ability of this technique to suppress noise and capture flow maps was demonstrated by imaging microflow in an ex-vivo porcine coronary artery model, by nailfold capillary imaging and in-vivo microvessel imaging from within the human coronary sinus.OCIS codes: (170.4500) Optical coherence tomography, (170.3880) Medical and biological imaging     

Fully automated lumen and vessel contour segmentation in intravascular ultrasound datasets

Segmentation of lumen and vessel contours in intravascular ultrasound (IVUS) pullbacks is an arduous and time-consuming task, which demands adequately trained human resources. In the present study, we propose a machine learning approach to automatically extract lumen and vessel boundaries from IVUS datasets. The proposed approach relies on the concatenation of a deep neural network to deliver a preliminary segmentation, followed by a Gaussian process (GP) regressor to construct the final lumen and vessel contours. A multi-frame convolutional neural network (MFCNN) exploits adjacency information present in longitudinally neighboring IVUS frames, while the GP regression method filters high-dimensional noise, delivering a consistent representation of the contours. Overall, 160 IVUS pullbacks (63 patients) from the IBIS-4 study (Integrated Biomarkers and Imaging Study-4, Trial NCT00962416), were used in the present work. The MFCNN algorithm was trained with 100 IVUS pullbacks (8427 manually segmented frames), was validated with 30 IVUS pullbacks (2583 manually segmented frames) and was blindly tested with 30 IVUS pullbacks (2425 manually segmented frames). Image and contour metrics were used to characterize model performance by comparing ground truth (GT) and machine learning (ML) contours. Median values (interquartile range, IQR) of the Jaccard index for lumen and vessel were 0.913, [0.882,0.935] and 0.940, [0.917,0.957], respectively. Median values (IQR) of the Hausdorff distance for lumen and vessel were $0.196\text{mm}$, $[0.146,0.275]\text{mm}$ and $0.163\text{mm}$, $[0.122,0.234]\text{mm}$, respectively. Also, the mean value of lumen area predictions, and limits of agreement were $-0.19{\text{mm}}^2$, $[1.1,-1.5]{\text{mm}}^2$, while the mean value and limits of agreement of plaque burden were 0.0022, $[0.082,-0.078]$. The results obtained with the model developed in this work allow us to conclude that the proposed machine learning approach delivers accurate segmentations in terms of image metrics, contour metrics and clinically relevant variables, enabling its use in clinical routine by mitigating the costs involved in the manual management of IVUS datasets.