Very long-term follow-up of strut apposition and tissue coverage with Biolimus A9 stents analyzed by optical coherence tomography

First generation drug-eluting stents (DES) are associated with reduced in-stent restenosis but significant increased risk of very late stent thrombosis (VLST). The absence of polymer in DES systems may reduce the occurrence of VLST. Optic coherence tomography (OCT) has been used for stent analysis as a surrogate safety endpoint. This study aimed to assess the long-term follow up of strut apposition and tissue coverage of BioMatrix? DES by OCT. 20 patients undergoing BioMatrix? DES (n = 15) or S-Stent? BMS (n = 5) implantation were followed for at least 5 years and evaluated by quantitative coronary angiography, intravascular ultrasound, and OCT. The difference between the stent types was evaluated by nonparametric Mann–Whitney U test while categorical variables were evaluated by Fisher exact test. Rates of in-stent late loss were similar between groups [0.40 (0.21;0.77) vs. 0.68 (0.66; 0.82) mm, p = 0.205, for BioMatrix? and S-Stent?, respectively]. The vessel, stent and lumen volumes did not differ between groups. Patients treated with BioMatrix? had significantly less stent obstruction [5.6 (4.4;9.7) vs. 28.6 (24.7;29.0) %, p = 0.001]. OCT analysis of 12 stents (Biomatrix? = 9 and S-Stent? = 3) demonstrated 126 (8.7 %) uncovered struts in the BioMatrix? group compared to 23 (4.0 %) in the S-Stent? group (p = 0.297), being the majority of them well apposed (117/126 and 21/23, respectively, p = 0.292). Only 9 (0.6 %) struts in the DES and 2 (0.4 %) struts in the BMS groups were simultaneously uncovered and malapposed (p = 0.924). BioMatrix? DES was associated with lower rates of in-stent obstruction, and similar percentage of neointimal coverage on struts and of complete strut apposition.     

Optical coherence tomography endpoints in stent clinical investigations: strut coverage

Late stent thrombosis (LST) and very LST (VLST) are infrequent complications after drug-eluting stent (DES) implantation, but they carry a significant risk for patients. Delayed healing, which may be represented by incomplete stent coverage, has been observed in necropsy vessel specimens treated with DES. As a result, in vivo assessment of stent coverage, as well as stent apposition using optical coherence tomography (OCT), have been recently used as surrogate safety endpoints in clinical trials testing DES platforms. By adopting strut coverage assessed by OCT, one can assess the safety profile of the new generation of DES in preregistration studies. This article focuses on stent strut coverage as a central predictor of late DES thrombosis from the histopathological point of view, discusses the limitations of the current imaging modalities and presents the technical characteristics of OCT for the detection of neointimal coverage after stent implantation. We also review the preclinical and clinical investigations using this novel imaging modality.     

Automatic segmentation of the choroid in enhanced depth imaging optical coherence tomography images

Enhanced Depth Imaging (EDI) optical coherence tomography (OCT) provides high-definition cross-sectional images of the choroid in vivo, and hence is used in many clinical studies. However, the quantification of the choroid depends on the manual labelings of two boundaries, Bruch’s membrane and the choroidal-scleral interface. This labeling process is tedious and subjective of inter-observer differences, hence, automatic segmentation of the choroid layer is highly desirable. In this paper, we present a fast and accurate algorithm that could segment the choroid automatically. Bruch’s membrane is detected by searching the pixel with the biggest gradient value above the retinal pigment epithelium (RPE) and the choroidal-scleral interface is delineated by finding the shortest path of the graph formed by valley pixels using Dijkstra’s algorithm. The experiments comparing automatic segmentation results with the manual labelings are conducted on 45 EDI-OCT images and the average of Dice’s Coefficient is 90.5%, which shows good consistency of the algorithm with the manual labelings. The processing time for each image is about 1.25 seconds.OCIS codes: (100.0100) Image processing, (110.4500) Optical coherence tomography, (100.2960) Image analysis, (170.4470) Ophthalmology     

Relationship between endothelial vasomotor function and strut coverage after implantation of drug-eluting stent assessed by optical coherence tomography

The use of drug-eluting stent (DES) has been associated with incomplete endothelialization and coronary endothelial dysfunction. However, the relationship between endothelial vasomotor function and strut coverage evaluated by optical coherence tomography (OCT) has not been sufficiently assessed. Therefore, we evaluated the relationship between endothelial vasomotor function and the degree of stent strut coverage after DES implantation. Coronary angiography and OCT were performed in 112 patients at the 6-month follow-up after DES implantation. The patients were divided into tertiles according to the degree of strut coverage as was assessed by OCT. Endothelial vasomotor function was evaluated with intracoronary infusion of incremental doses of acetylcholine (Ach; 10^?8–10^?6 mol/L). Vascular responses at the proximal and distal segments to the stent margin were evaluated by quantitative coronary angiography analysis before and after Ach infusion. The percentage of uncovered struts in tertiles 1–3 was 4.2 ± 3.3, 17.3 ± 4.2 and 44.5 ± 14.4 %, respectively, (p < 0.001). The percentage of maximal vasoconstriction in tertiles 1–3 was 8.3, 9.1 and 8.1 % at proximal segment to the stent margin (p = 0.95), respectively, and 13.9, 11.1 and 14.2 % at distal segment to the stent margin (p = 0.74), respectively. The percentage of uncovered struts was not correlated with the degree of vasomotor function (r = ?0.01, p = 0.92 at the proximal segment; r = ?0.07, p = 0.47 at the distal segment). The percentage of strut coverage was not associated with the degree of abnormal vasoconstriction in response to intracoronary infusion of Ach 6 months after DES implantation.     

Automatic segmentation of choroidal thickness in optical coherence tomography

The assessment of choroidal thickness from optical coherence tomography (OCT) images of the human choroid is an important clinical and research task, since it provides valuable information regarding the eye’s normal anatomy and physiology, and changes associated with various eye diseases and the development of refractive error. Due to the time consuming and subjective nature of manual image analysis, there is a need for the development of reliable objective automated methods of image segmentation to derive choroidal thickness measures. However, the detection of the two boundaries which delineate the choroid is a complicated and challenging task, in particular the detection of the outer choroidal boundary, due to a number of issues including: (i) the vascular ocular tissue is non-uniform and rich in non-homogeneous features, and (ii) the boundary can have a low contrast. In this paper, an automatic segmentation technique based on graph-search theory is presented to segment the inner choroidal boundary (ICB) and the outer choroidal boundary (OCB) to obtain the choroid thickness profile from OCT images. Before the segmentation, the B-scan is pre-processed to enhance the two boundaries of interest and to minimize the artifacts produced by surrounding features. The algorithm to detect the ICB is based on a simple edge filter and a directional weighted map penalty, while the algorithm to detect the OCB is based on OCT image enhancement and a dual brightness probability gradient. The method was tested on a large data set of images from a pediatric (1083 B-scans) and an adult (90 B-scans) population, which were previously manually segmented by an experienced observer. The results demonstrate the proposed method provides robust detection of the boundaries of interest and is a useful tool to extract clinical data.OCIS codes: (100.0100) Image processing, (100.2960) Image analysis, (110.4500) Optical coherence tomography, (170.4470) Ophthalmology     

Quantitative assessment of the stent/scaffold strut embedment analysis by optical coherence tomography

The degree of stent/scaffold embedment could be a surrogate parameter of the vessel wall-stent/scaffold interaction and could have biological implications in the vascular response. We have developed a new specific software for the quantitative evaluation of embedment of struts by optical coherence tomography (OCT). In the present study, we described the algorithm of the embedment analysis and its reproducibility. The degree of embedment was evaluated as the ratio of the embedded part versus the whole strut height and subdivided into quartiles. The agreement and the inter- and intra-observer reproducibility were evaluated using the kappa and the interclass correlation coefficient (ICC). A total of 4 pullbacks of OCT images in 4 randomly selected coronary lesions with 3.0 × 18 mm devices [2 lesions with Absorb BVS and 2 lesions with XIENCE (both from Abbott Vascular, Santa Clara, CA, USA)] from Absorb Japan trial were evaluated by two investigators with QCU-CMS software version 4.69 (Leiden University Medical Center, Leiden, The Netherlands). Finally, 1481 polymeric struts in 174 cross-sections and 1415 metallic struts in 161 cross-sections were analyzed. Inter- and intra-observer reproducibility of quantitative measurements of embedment ratio and categorical assessment of embedment in Absorb BVS and XIENCE had excellent agreement with ICC ranging from 0.958 to 0.999 and kappa ranging from 0.850 to 0.980. The newly developed embedment software showed excellent reproducibility. Computer-assisted embedment analysis could be a feasible tool to assess the strut penetration into the vessel wall that could be a surrogate of acute injury caused by implantation of devices.     

Reproducibility of qualitative assessment of stent struts coverage by optical coherence tomography

Assessment of stent strut coverage by optical coherence tomography (OCT) is not standardized. The methodology most commonly used is based on a visual binary qualitative assessment (strut covered or not). However, the influence of magnification (zoom setting) to the inter- and intra-observer agreements has not yet been evaluated. Aim of our study was therefore to evaluate the agreements of this approach, taking into account various zoom settings. 126 struts from 10 selected frames were independently evaluated by four observers using a stepwise approach increasing the zoom setting as following: (1) full view of the lumen (FV), (2) half view of the lumen (HV) and (3) a quarter view of the lumen (QV). Intra- and inter-observer agreements (κ) were assessed. The rate of uncoverage was determined for each strut as the number of times it was defined as uncovered divided by the total number of observations (maximum 12 = 3 zoom settings × 4 analysts) and expressed as percentage. The inter-observer κ values (mean [range]) were 0.32 [0.07–0.63], 0.40 [0.18–0.69] and 0.33 [0.09–0.6], within FV, HV and QV respectively. The intra-observer κ values were 0.60 [0.50–0.70], 0.75 [0.75–0.76] and 0.60 [0.50–0.70], within FV, HV and QV respectively. By increasing zoom setting the κ value of intra-observer agreement was 0.74 [0.58–0.83] (from FV to HV), 0.70 [0.56–0.83] (from HV to QV) and 0.70 [0.37–0.86] (from FV to QV). Overall, the rate of uncoverage was 15.5% [8.3–100%]. The OCT qualitative evaluation of strut coverage has wide inter and intra-observer agreements and is dependent of the zoom setting used during the analysis. A more reproducible approach would be needed to eventually increase the probability to link uncovered struts with clinical events.     

Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema

We present a fully automatic algorithm to identify fluid-filled regions and seven retinal layers on spectral domain optical coherence tomography images of eyes with diabetic macular edema (DME). To achieve this, we developed a kernel regression (KR)-based classification method to estimate fluid and retinal layer positions. We then used these classification estimates as a guide to more accurately segment the retinal layer boundaries using our previously described graph theory and dynamic programming (GTDP) framework. We validated our algorithm on 110 B-scans from ten patients with severe DME pathology, showing an overall mean Dice coefficient of 0.78 when comparing our KR + GTDP algorithm to an expert grader. This is comparable to the inter-observer Dice coefficient of 0.79. The entire data set is available online, including our automatic and manual segmentation results. To the best of our knowledge, this is the first validated, fully-automated, seven-layer and fluid segmentation method which has been applied to real-world images containing severe DME.OCIS codes: (100.0100) Image processing, (170.4500) Optical coherence tomography, (170.4470) Ophthalmology     

Very early tissue coverage after drug-eluting stent implantation: an optical coherence tomography study

The aim of this study was to evaluate neointimal coverage in the very early phase after second-generation drug-eluting stent (DES) implantation using optical coherence tomography (OCT). Patients who underwent staged percutaneous coronary intervention within 30 days after DES implantation were enrolled. OCT was performed to observe DES previously implanted. The median time interval from implantation to OCT examination was 21.5 days. A total of 10,625 struts of 54 stents (52 everolimus-eluting stents and 2 zotarolimus-eluting stents) in 42 lesions were analyzed. Strut tissue coverage was observed in 71.1?±?19.2?% of the struts, malapposed struts in 2.56?±?3.37?%, strut tissue coverage at the side branch orifice in 10.6?±?17.2?%, and struts with protrusion in 0.95?±?3.46?%. Mean tissue thickness on the covered struts was 39.8?±?14.2 µm. The percentage of stent coverage was significantly lower in the overlapping segments than in the non-overlapping segments (48.4?±?17.5?% vs. 74.4?±?20.2?%, P?<?0.05). Most of the stent struts were covered by tissue within 30 days after second-generation DES implantation. However, the percentage of strut coverage was lower in the overlapping segments than in the non-overlapping segments, suggesting that very early interruption of dual antiplatelet therapy might result in increased risk of stent thrombosis, even in second-generation DES.     

Intravascular optical coherence tomography measurement of size and apposition of metallic stents

Effect of beam size and catheter position on the apparent size and apposition of metallic stent struts in IVOCT images were examined. Micro-CT data was employed to determine light - stent strut interactions. Simulated results suggest that location of the reflecting regions depend on relative orientation and position of stent struts to the IVOCT beam. Erroneous stent apposition measurements can occur when the IVOCT catheter is at an eccentric position. A method that mitigates stent strut apposition measurement errors is proposed.OCIS codes: (110.0110) Imaging systems, (110.4500) Optical coherence tomography     

Tissue self-attention network for the segmentation of optical coherence tomography images on the esophagus

Automatic segmentation of layered tissue is the key to esophageal optical coherence tomography (OCT) image processing. With the advent of deep learning techniques, frameworks based on a fully convolutional network are proved to be effective in classifying pixels on images. However, due to speckle noise and unfavorable imaging conditions, the esophageal tissue relevant to the diagnosis is not always easy to identify. An effective approach to address this problem is extracting more powerful feature maps, which have similar expressions for pixels in the same tissue and show discriminability from those from different tissues. In this study, we proposed a novel framework, called the tissue self-attention network (TSA-Net), which introduces the self-attention mechanism for esophageal OCT image segmentation. The self-attention module in the network is able to capture long-range context dependencies from the image and analyzes the input image in a global view, which helps to cluster pixels in the same tissue and reveal differences of different layers, thus achieving more powerful feature maps for segmentation. Experiments have visually illustrated the effectiveness of the self-attention map, and its advantages over other deep networks were also discussed.     

Correlation of angiographic late loss with neointimal coverage of drug-eluting stent struts on follow-up optical coherence tomography

Minimal data have been published on the correlation between angiographic late loss (LL) and incomplete neointimal coverage of struts after drug-eluting stent (DES) implantation. Therefore, we evaluated the relationship between angiographic LL and the percentage of uncovered struts on follow-up optical coherence tomography (OCT) images, in all cross-sections of the lesions. From the OCT registry database, 219 lesions without restenosis after DES implantation were divided into tertiles based on angiographic LL: tertile I (LL?≤?0.26?mm), tertile II (0.26?6.0%) were defined as highly uncovered; in an independent analysis, lesions without any uncovered strut(s) were defined as completely covered. Higher percentages of uncovered struts were observed in tertile I than in both tertile II and III (10.3?±?12.8% vs. 4.2?±?7.4% vs. 2.4?±?5.1%, respectively; P?相似文献   

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.     

Evaluation neointimal coverage in patients with coronary artery aneurysm formation after drug-eluting stent implantation by optical coherence tomography

The neointimal coverage in patients with coronary artery aneurysms (CAA) formation after drug eluting stent (DES) implantation is not clear. Total of 175 patients who had been implanted DES were identified. Patients were divided into the CAA group (n = 31) and non-CAA group (n = 144) based on the results of the coronary angiography. The cardiac events including angina and acute myocardial infarction were noted, in addition, the neointimal thickness and the frequence of strut malapposition and strut uncoverage were noted. A greater proportion of incomplete neointimal coverage (17.17 vs. 1.9 %, P < 0.001) and malapposition struts (18.2 vs. 1.38%, P < 0.001) were observed in the CAA group. 8 patients in CAA group underwent OCT examination twice in the period of follow-up. The proportion of incomplete neointimal coverage increased significantly as compared the second OCT results with the first examination (18.45 vs. 2.66 %, P < 0.001). Hyperplasia neointimal desquamated from struts and acquired struts incomplete neointimal coverage were detected. Patients with CAA had a higher frequency of cardiac events including angina pectoris (25.81 vs. 6.25 %, P = 0.001) and acute myocardial infarction (9.68 vs. 0.13 %, P = 0.002) and thrombosis (16.13 vs. 0.69 %, P < 0.001). The longitudinal length of CAA in cardiac event group was significantly longer than no cardiac event group (20.0 ± 9.07 vs. 12.05 ± 5.38 mm, P = 0.005). CAA formation after DES implantation frequently associated with cardiac events as a result of stent malapposition and incomplete neointimal coverage. Acquired incomplete neointimal coverage associated with CAA formation.     

Automated quantification of lung structures from optical coherence tomography images

Characterization of the size of lung structures can aid in the assessment of a range of respiratory diseases. In this paper, we present a fully automated segmentation and quantification algorithm for the delineation of large numbers of lung structures in optical coherence tomography images, and the characterization of their size using the stereological measure of median chord length. We demonstrate this algorithm on scans acquired with OCT needle probes in fresh, ex vivo tissues from two healthy animal models: pig and rat. Automatically computed estimates of lung structure size were validated against manual measures. In addition, we present 3D visualizations of the lung structures using the segmentation calculated for each data set. This method has the potential to provide an in vivo indicator of structural remodeling caused by a range of respiratory diseases, including chronic obstructive pulmonary disease and pulmonary fibrosis.OCIS codes: (110.4500) Optical coherence tomography, (100.0100) Image processing, (100.2960) Image analysis     

Sparsity based denoising of spectral domain optical coherence tomography images

In this paper, we make contact with the field of compressive sensing and present a development and generalization of tools and results for reconstructing irregularly sampled tomographic data. In particular, we focus on denoising Spectral-Domain Optical Coherence Tomography (SDOCT) volumetric data. We take advantage of customized scanning patterns, in which, a selected number of B-scans are imaged at higher signal-to-noise ratio (SNR). We learn a sparse representation dictionary for each of these high-SNR images, and utilize such dictionaries to denoise the low-SNR B-scans. We name this method multiscale sparsity based tomographic denoising (MSBTD). We show the qualitative and quantitative superiority of the MSBTD algorithm compared to popular denoising algorithms on images from normal and age-related macular degeneration eyes of a multi-center clinical trial. We have made the corresponding data set and software freely available online.     

Quantitative characterization of human breast tissue based on deep learning segmentation of 3D optical coherence tomography images

In this study, we performed dual-modality optical coherence tomography (OCT) characterization (volumetric OCT imaging and quantitative optical coherence elastography) on human breast tissue specimens. We trained and validated a U-Net for automatic image segmentation. Our results demonstrated that U-Net segmentation can be used to assist clinical diagnosis for breast cancer, and is a powerful enabling tool to advance our understanding of the characteristics for breast tissue. Based on the results obtained from U-Net segmentation of 3D OCT images, we demonstrated significant morphological heterogeneity in small breast specimens acquired through diagnostic biopsy. We also found that breast specimens affected by different pathologies had different structural characteristics. By correlating U-Net analysis of structural OCT images with mechanical measurement provided by quantitative optical coherence elastography, we showed that the change of mechanical properties in breast tissue is not directly due to the change in the amount of dense or porous tissue.     

Automatic dynamic tear meniscus measurement in optical coherence tomography

An image processing algorithm is developed for quantitative assessment of tear meniscus dynamics from continuous optical coherence tomography (OCT) measurements. Clinical utility of dynamic OCT tear meniscus measurement is assessed in studies of tear meniscus parameters. The results indicate that any apparent changes in the early post-blink phase meniscus parameters are essentially related to the longitudinal movements of the eye and not to the formation of tear meniscus corresponding to tear film build-up. Dynamic acquisition of tear film meniscus is essential for providing reliable estimates of its parameters such as height, depth, and area.OCIS codes: (100.0100) Image processing, (100.2960) Image analysis, (170.4500) Optical coherence tomography, (170.4470) Ophthalmology