Background and objective: Although the association of wall shear stress (WSS) with coronary artery disease has been well studied, that of mechanical wall stress (MWS) is mainly overlooked. In this study, we performed in-silico artery-specific modeling to investigate the involvement of both MWS and WSS in coronary artery disease. Methods: Fifteen coronary arteries from five adult familial hypercholesterolemic pigs were imaged by coronary computed tomography angiography, intravascular ultrasound, and optical coherence tomography at three time points (3, 9, and 12 months). Local WSS and MWS in 3 mm/45° sectors were determined using artery-specific computational models. The relationship of WSS and MWS with wall thickness change (ΔWT) over time was statistically analyzed using Generalized Linear Mixed models. Results: A positive ΔWT was measured in all sectors, where plaque sectors presented a greater ΔWT rate compared to plaque-free sectors. In plaque-free sectors, low WSS was associated with a higher ΔWT rate (p < 0.001). In plaque sectors, high MWS was associated with a higher ΔWT rate (p < 0.05), where ΔWT rate was, although slightly, even greater in the plaque sectors with lipid-rich necrotic core (p < 0.05). Conclusions: Our results from in-silico coronary-specific models suggest that WSS and MWS may play a dominant role at different stages of coronary artery disease. WSS may be more critical in the early stages of plaque formation while MWS might have greater significance in the progression of existing plaques.

Safety and efficacy of coronary drug-eluting stents (DES) are often preclinically tested using healthy or minimally diseased swine. These generally show significant fibrotic neointima at follow-up, while in patients, incomplete healing is often observed. The aim of this study was to investigate neointima responses to DES in swine with significant coronary atherosclerosis. Adult familial hypercholesterolemic swine (n = 6) received a high fat diet to develop atherosclerosis. Serial OCT was performed before, directly after, and 28 days after DES implantation (n = 14 stents). Lumen, stent and plaque area, uncovered struts, neointima thickness and neointima type were analyzed for each frame and averaged per stent. Histology was performed to show differences in coronary atherosclerosis. A range of plaque size and severity was found, from healthy segments to lipid-rich plaques. Accordingly, neointima responses ranged from uncovered struts, to minimal neointima, to fibrotic neointima. Lower plaque burden resulted in a fibrotic neointima at follow-up, reminiscent of minimally diseased swine coronary models. In contrast, higher plaque burden resulted in minimal neointima and more uncovered struts at follow-up, similarly to patients’ responses. The presence of lipid-rich plaques resulted in more uncovered struts, which underscores the importance of advanced disease when performing safety and efficacy testing of DES.

To quantify the impact of cardiac motion on stent length measurements with Optical Coherence Tomography (OCT) and to demonstrate in vivo OCT imaging of implanted stents, without motion artefacts. The study consists of: clinical data evaluation, simulations and in vivo tests. A comparison between OCT-measured and nominal stent lengths in 101 clinically acquired pullbacks was carried out, followed by a simulation of the effect of cardiac motion on stent length measurements, experimentally and computationally. Both a commercial system and a custom OCT, capable of completing a pullback between two consecutive ventricular contractions, were employed. A 13 mm long stent was implanted in the left anterior descending branch of two atherosclerotic swine and imaged with both OCT systems. The analysis of the clinical OCT images yielded an average difference of 1.1 ± 1.6 mm, with a maximum difference of 7.8 mm and the simulations replicated the statistics observed in clinical data. Imaging with the custom OCT, yielded an RMS error of 0.14 mm at 60 BPM with the start of the acquisition synchronized to the cardiac cycle. In vivo imaging with conventional OCT yielded a deviation of 1.2 mm, relative to the length measured on ex-vivo micro-CT, while the length measured in the pullback acquired by the custom OCT differed by 0.20 mm. We demonstrated motion artefact-free OCT-imaging of implanted stents, using ECG triggering and a rapid pullback.

Percutaneous coronary intervention is a minimally invasive procedure that is usually performed under image guidance using X-ray angiograms in which coronary arteries are opacified with contrast agent. In X-ray images, 3D objects are projected on a 2D plane, generating semi-transparent layers that overlap each other. The overlapping of structures makes robust automatic information processing of the X-ray images, such as vessel extraction which is highly relevant to support smart image guidance, challenging. In this paper, we propose an automatic online layer separation approach that robustly separates interventional X-ray angiograms into three layers: a breathing layer, a quasi-static layer and a vessel layer that contains information of coronary arteries and medical instruments. The method uses morphological closing and an online robust PCA algorithm to separate the three layers. The proposed layer separation method ran fast and was demonstrated to significantly improve the vessel visibility in clinical X-ray images and showed better performance than other related online or prospective approaches. The potential of the proposed approach was demonstrated by enhancing contrast of vessels in X-ray images with low vessel contrast, which would facilitate the use of reduced amount of contrast agent to prevent contrast-induced side effects.

Purpose: Quantitative and automatic analysis of intracoronary optical coherence tomography images is useful and time-saving to assess cardiovascular risk in the clinical arena. Methods: First, the interfaces of the intima, media, and adventitia layers are segmented, by means of an original front propagation scheme, running in a 4D multi-parametric space, to simultaneously extract three non-crossing contours in the initial cross-sectional image. Second, information resulting from the tentative contours is exploited by a machine learning approach to identify healthy and diseased regions of the arterial wall. The framework is fully automatic. Results: The method was applied to 40 patients from two different medical centers. The framework was trained on 140 images and validated on 260 other images. For the contour segmentation method, the average segmentation errors were 29±46μm for the intima–media interface, 30±50μm for the media–adventitia interface, and 50±64μm for the adventitia–periadventitia interface. The classification method demonstrated a good accuracy, with a median Dice coefficient equal to 0.93 and an interquartile range of (0.78–0.98). Conclusion: The proposed framework demonstrated promising offline performances and could potentially be translated into a reliable tool for various clinical applications, such as quantification of tissue layer thickness and global summarization of healthy regions in entire pullbacks.