GPU-accelerated framework for intracoronary optical coherence tomography imaging at the push of a button

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.

Automatic segmentation of optical coherence tomography pullbacks of coronary arteries treated with bioresorbable vascular scaffolds: Application to hemodynamics modeling

Background / Objectives

Automatic algorithms for stent struts segmentation in optical coherence tomography (OCT) images of coronary arteries have been developed over the years, particularly with application on metallic stents. The aim of this study is three-fold: (1) to develop and to validate a segmentation algorithm for the detection of both lumen contours and polymeric bioresorbable scaffold struts from 8-bit OCT images, (2) to develop a method for automatic OCT pullback quality assessment, and (3) to demonstrate the applicability of the segmentation algorithm for the creation of patient-specific stented coronary artery for local hemodynamics analysis.

Methods

The proposed OCT segmentation algorithm comprises four steps: (1) image pre-processing, (2) lumen segmentation, (3) stent struts segmentation, (4) strut-based lumen correction. This segmentation process is then followed by an automatic OCT pullback image quality assessment. This method classifies the OCT pullback image quality as ‘good’ or ‘poor’ based on the number of regions detected by the stent segmentation. The segmentation algorithm was validated against manual segmentation of 1150 images obtained from 23 in vivo OCT pullbacks.

Results

When considering the entire set of OCT pullbacks, lumen segmentation showed results comparable with manual segmentation and with previous studies (sensitivity ~97%, specificity ~99%), while stent segmentation showed poorer results compared to manual segmentation (sensitivity ~63%, precision ~83%). The OCT pullback quality assessment algorithm classified 7 pullbacks as ‘poor’ quality cases. When considering only the ‘good’ classified cases, the performance indexes of the scaffold segmentation were higher (sensitivity >76%, precision >86%).

Conclusions

This study proposes a segmentation algorithm for the detection of lumen contours and stent struts in low quality OCT images of patients treated with polymeric bioresorbable scaffolds. The segmentation results were successfully used for the reconstruction of one coronary artery model that included a bioresorbable scaffold geometry for computational fluid dynamics analysis.

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.

Reconstruction of stented coronary arteries from optical coherence tomography images: Feasibility, validation, and repeatability of a segmentation method

Optical coherence tomography (OCT) is an established catheter-based imaging modality for the assessment of coronary artery disease and the guidance of stent placement during percutaneous coronary intervention. Manual analysis of large OCT datasets for vessel contours or stent struts detection is time-consuming and unsuitable for real-time applications. In this study, a fully automatic method was developed for detection of both vessel contours and stent struts. The method was applied to in vitro OCT scans of eight stented silicone bifurcation phantoms for validation purposes. The proposed algorithm comprised four main steps, namely pre-processing, lumen border detection, stent strut detection, and three-dimensional point cloud creation. The algorithm was validated against manual segmentation performed by two independent image readers. Linear regression showed good agreement between automatic and manual segmentations in terms of lumen area (r>0.99). No statistically significant differences in the number of detected struts were found between the segmentations. Mean values of similarity indexes were >95% and >85% for the lumen and stent detection, respectively. Stent point clouds of two selected cases, obtained after OCT image processing, were compared to the centerline points of the corresponding stent reconstructions from micro computed tomography, used as ground-truth. Quantitative comparison between the corresponding stent points resulted in median values of ~150 μm and ~40 μm for the total and radial distances of both cases, respectively. The repeatability of the detection method was investigated by calculating the lumen volume and the mean number of detected struts per frame for seven repeated OCT scans of one selected case. Results showed low deviation of values from the median for both analyzed quantities. In conclusion, this study presents a robust automatic method for detection of lumen contours and stent struts from OCT as supported by focused validation against both manual segmentation and micro computed tomography and by good repeatability.

Single cardiac cycle three-dimensional intracoronary optical coherence tomography

While high-speed intracoronary optical coherence tomography (OCT) provides three-dimensional (3D) visualization of coronary arteries in vivo, imaging speeds remain insufficient to avoid motion artifacts induced by heartbeat, limiting the clinical utility of OCT. In this paper, we demonstrate development of a high-speed intracoronary OCT system (frame rate: 500 frames/s, pullback speed: 100 mm/s) along with prospective electrocardiogram (ECG) triggering technology, which enabled volumetric imaging of long coronary segments within a single cardiac cycle (70 mm pullback in 0.7 s) with minimal cardiac motion artifact. This technology permitted detailed visualization of 3D architecture of the coronary arterial wall of a swine in vivo and fine structure of the implanted stent.

Characterization of lipid-rich plaques using spectroscopic optical coherence tomography

Intravascular optical coherence tomography (IV-OCT) is a high-resolution imaging method used to visualize the internal structures of walls of coronary arteries in vivo. However, accurate characterization of atherosclerotic plaques with gray-scale IV-OCT images is often limited by various intrinsic artifacts. In this study, we present an algorithm for characterizing lipid-rich plaques with a spectroscopic OCT technique based on a Gaussian center of mass (GCOM) metric. The GCOM metric, which reflects the absorbance properties of lipids, was validated using a lipid phantom. In addition, the proposed characterization method was successfully demonstrated in vivo using an atherosclerotic rabbit model and was found to have a sensitivity and specificity of 94.3% and 76.7% for lipid classification, respectively.

Intracoronary dual-modal optical coherence tomography-near-infrared fluorescence structural-molecular imaging with a clinical dose of indocyanine green for the assessment of high-risk plaques and stent-associated inflammation in a beating coronary artery

AIMS

Inflammation plays essential role in development of plaque disruption and coronary stent-associated complications. This study aimed to examine whether intracoronary dual-modal optical coherence tomography (OCT)-near-infrared fluorescence (NIRF) structural-molecular imaging with indocyanine green (ICG) can estimate inflammation in swine coronary artery.

METHODS AND RESULTS

After administration of clinically approved NIRF-enhancing ICG (2.0 mg/kg) or saline, rapid coronary imaging (20 mm/s pullback speed) using a fully integrated OCT-NIRF catheter was safely performed in 12 atheromatous Yucatan minipigs and in 7 drug-eluting stent (DES)-implanted Yorkshire pigs. Stronger NIRF activity was identified in OCT-proven high-risk plaque compared to normal or saline-injected controls (P = 0.0016), which was validated on ex vivo fluorescence reflectance imaging. In vivo plaque target-to-background ratio (pTBR) was much higher in inflamed lipid-rich plaque compared to fibrous plaque (P < 0.0001). In vivo and ex vivo peak pTBRs correlated significantly (P < 0.0022). In vitro cellular ICG uptake and histological validations corroborated the OCT-NIRF findings in vivo. Indocyanine green colocalization with macrophages and lipids of human plaques was confirmed with autopsy atheroma specimens. Two weeks after DES deployment, OCT-NIRF imaging detected strong NIRF signals along stent struts, which was significantly higher than baseline (P = 0.0156). Histologically, NIRF signals in peri-strut tissue co-localized well with macrophages.

CONCLUSION

The OCT-NIRF imaging with a clinical dose of ICG was feasible to accurately assess plaque inflammation and DES-related inflammation in a beating coronary artery. This highly translatable dual-modal molecular-structural imaging strategy could be relevant for clinical intracoronary estimation of high-risk plaques and DES biology.