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; r2 = 0.98, P\ 0.0001) and the stent cross-section area (mean difference = 0.10 ± 1.28 mm2; r2 = 0.85, P value\ 0.0001). The average number of detected struts was 10.4 ± 2.9 per crosssection when the expert identified 10.5 ± 2.8 (r2 = 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

A New 3-D automated computational method to evaluate in-stent neointimal hyperplasia in in-vivo intravascular optical coherence tomography pullbacks

Abstract. Detection of stent struts imaged in vivo by optical coherence tomography (OCT) after percutaneous coronary interventions (PCI) and quantification of in-stent neointimal hyperplasia (NIH) are important. In this paper, we present a new computational method to facilitate the physician in this endeavor to assess and compare new (drug-eluting) stents. We developed a new algorithm for stent strut detection and utilized splines to reconstruct the lumen and stent boundaries which provide automatic measurements of NIH thickness, lumen and stent area. Our original approach is based on the detection of stent struts unique characteristics: bright reflection and shadow behind. Furthermore, we present for the first time to our knowledge a rotation correction method applied across OCT cross-section images for 3D reconstruction and visualization of reconstructed lumen and stent boundaries for further analysis in the longitudinal dimension of the coronary artery. Our experiments over OCT cross-sections taken from 7 patients presenting varying degrees of NIH after PCI illustrate a good agreement between the computer method and expert evaluations: Bland-Altmann analysis revealed a mean difference for lumen cross-section area of 0.11 ± 0.70mm2 and for the stent cross-section area of 0.10 ± 1.28mm2

A framework for computational fluid dynamic analyses of patient-specific stented coronary arteries from optical coherence tomography images

The clinical challenge of percutaneous coronary interventions (PCI) is highly dependent on the recognition of the coronary anatomy of each individual. The classic imaging modality used for PCI is angiography, but advanced imaging techniques that are routinely performed during PCI, like optical coherence tomography (OCT), may provide detailed knowledge of the pre-intervention vessel anatomy as well as the post-procedural assessment of the specific stent-to-vessel interactions. Computational fluid dynamics (CFD) is an emerging investigational tool in the setting of optimization of PCI results. In this study, an OCT-based reconstruction method was developed for the execution of CFD simulations of patient-specific coronary artery models which include the actual geometry of the implanted stent. The method was applied to a rigid phantom resembling a stented segment of the left anterior descending coronary artery. The segmentation algorithm was validated against manual segmentation. A strong correlation was found between automatic and manual segmentation of lumen in terms of area values. Similarity indices resulted >96% for the lumen segmentation and >77% for the stent strut segmentation. The 3D reconstruction achieved for the stented phantom was also assessed with the geometry provided by X-ray computed micro tomography scan, used as ground truth, and showed the incidence of distortion from catheter-based imaging techniques. The 3D reconstruction was successfully used to perform CFD analyses, demonstrating a great potential for patient-specific investigations. In conclusion, OCT may represent a reliable source for patient-specific CFD analyses which may be optimized using dedicated automatic segmentation algorithms

Automated analysis of fibrous cap in intravascular optical coherence tomography images of coronary arteries

Thin-cap fibroatheroma (TCFA) and plaque rupture have been recognized as the most frequent risk factor for thrombosis and acute coronary syndrome. Intravascular optical coherence tomography (IVOCT) can identify TCFA and assess cap thickness, which provides an opportunity to assess plaque vulnerability. We developed an automated method that can detect lipidous plaque and assess fibrous cap thickness in IVOCT images. This study analyzed a total of 4,360 IVOCT image frames of 77 lesions among 41 patients. To improve segmentation performance, preprocessing included lumen segmentation, pixel-shifting, and noise filtering on the raw polar (r, theta) IVOCT images. We used the DeepLab-v3 plus deep learning model to classify lipidous plaque pixels. After lipid detection, we automatically detected the outer border of the fibrous cap using a special dynamic programming algorithm and assessed the cap thickness. Our method provided excellent discriminability of lipid plaque with a sensitivity of 85.8% and A-line Dice coefficient of 0.837. By comparing lipid angle measurements between two analysts following editing of our automated software, we found good agreement by Bland-Altman analysis (difference 6.7+/-17 degree; mean 196 degree). Our method accurately detected the fibrous cap from the detected lipid plaque. Automated analysis required a significant modification for only 5.5% frames. Furthermore, our method showed a good agreement of fibrous cap thickness between two analysts with Bland-Altman analysis (4.2+/-14.6 micron; mean 175 micron), indicating little bias between users and good reproducibility of the measurement. We developed a fully automated method for fibrous cap quantification in IVOCT images, resulting in good agreement with determinations by analysts. The method has great potential to enable highly automated, repeatable, and comprehensive evaluations of TCFAs.Comment: 18 pages, 9 figure

Automated Accurate Lumen Segmentation Using L-mode Interpolation for Three-Dimensional Intravascular Optical Coherence Tomography

Intravascular optical coherence tomography (IVOCT) lumen-based computational flow dynamics (CFD) enables physiologic evaluations such as of the fractional flow reserve (FFR) and wall sheer stress. In this study, we developed an accurate, time-efficient method for extracting lumen contours of the coronary artery. The contours of cross-sectional images containing wide intimal discontinuities due to guide wire shadowing and large bifurcations were delineated by utilizing the natural longitudinal lumen continuity of the arteries. Our algorithm was applied to 5931 pre-intervention OCT images acquired from 40 patients. For a quantitative comparison, the images were also processed through manual segmentation (the reference standard) and automated ones utilizing cross-sectional and longitudinal continuities. The results showed that the proposed algorithm outperforms other schemes, exhibiting a strong correlation (R = 0.988) and overlapping and non-overlapping area ratios of 0.931 and 0.101, respectively. To examine the accuracy of the OCT-derived FFR calculated using the proposed scheme, a CFD simulation of a three-dimensional coronary geometry was performed. The strong correlation with a manual lumen-derived FFR (R = 0.978) further demonstrated the reliability and accuracy of our algorithm with potential applications in clinical settings.ope

The Correlation Between Texture Features and Fibrous Cap Thickness of Lipid-Rich Atheroma Based on Optical Coherence Tomography Imaging

Fibrous cap thickness (FCT) is seen as critical to plaque vulnerability. Therefore, the development of automatic algorithms for the quantification of FCT is for estimating cardiovascular risk of patients. Intravascular optical coherence tomography (IVOCT) is currently the only in vivo imaging modality with which FCT, the critical component of plaque vulnerability, can be assessed accurately. This study was aimed to discussion the correlation between the texture features of OCT images and the FCT in lipid-rich atheroma. Methods: Firstly, a full automatic segmentation algorithm based on unsupervised fuzzy c means (FCM) clustering with geometric constrains was developed to segment the ROIs of IVOCT images. Then, 32 features, which are associated with the structural and biochemical changes of tissue, were carried out to describe the properties of ROIs. The FCT in grayscale IVOCT images were manually measured by two independent observers. In order to analysis the correlation between IVOCT image features and manual FCT measurements, linear regression approach was performed. Results: Inter-observer agreement of the twice manual FCT measurements was excellent with an intraclass correlation coefficient (ICC) of 0.99. The correlation coefficient between each individual feature set and mean FCT of OCT images were 0.68 for FOS, 0.80 for GLCM, 0.74 for NGTDM, 0.72 for FD, 0.62 for IM and 0.58 for SP. The fusion image features of automatic segmented ROIs and FCT measurements improved the results significantly with a high correlation coefficient (r= 0.91, p<0.001). Conclusion The OCT images features demonstrated the perfect performances and could be used for automatic qualitative analysis and the identification of high-risk plaques instead manual FCT measurements

Dual modality intravascular optical coherence tomography (OCT) and near-infrared fluorescence (NIRF) imaging: a fully automated algorithm for the distance-calibration of NIRF signal intensity for quantitative molecular imaging

Intravascular optical coherence tomography (IVOCT) is a well-established method for the high-resolution investigation of atherosclerosis in vivo. Intravascular near-infrared fluorescence (NIRF) imaging is a novel technique for the assessment of molecular processes associated with coronary artery disease. Integration of NIRF and IVOCT technology in a single catheter provides the capability to simultaneously obtain co-localized anatomical and molecular information from the artery wall. Since NIRF signal intensity attenuates as a function of imaging catheter distance to the vessel wall, the generation of quantitative NIRF data requires an accurate measurement of the vessel wall in IVOCT images. Given that dual modality, intravascular OCT–NIRF systems acquire data at a very high frame-rate (>100 frames/s), a high number of images per pullback need to be analyzed, making manual processing of OCT–NIRF data extremely time consuming. To overcome this limitation, we developed an algorithm for the automatic distance-correction of dual-modality OCT–NIRF images. We validated this method by comparing automatic to manual segmentation results in 180 in vivo images from six New Zealand White rabbit atherosclerotic after indocyanine-green injection. A high Dice similarity coefficient was found (0.97 ± 0.03) together with an average individual A-line error of 22 µm (i.e., approximately twice the axial resolution of IVOCT) and a processing time of 44 ms per image. In a similar manner, the algorithm was validated using 120 IVOCT clinical images from eight different in vivo pullbacks in human coronary arteries. The results suggest that the proposed algorithm enables fully automatic visualization of dual modality OCT–NIRF pullbacks, and provides an accurate and efficient calibration of NIRF data for quantification of the molecular agent in the atherosclerotic vessel wall.National Institutes of Health (U.S.) (NIH R01HL093717)Merck & Co., Inc