The implantation of bioresorbable scaffolds (BRS) alters the local hemodynamic environment. Computational fluid dynamics (CFD) allows evaluation of local flow pattern, shear stress (SS) and Pressure_distal/ Pressure_approximal (Pd/Pa). The accuracy of CFD results relies to a great extent on the reconstruction of the 3D geometrical model. The aim of this study was to develop a new approach for in vivo reconstruction of coronary tree and BRS by fusion of Optical Coherence Tomography (OCT) and X-ray angiography. Ten patients enrolled in the BIFSORB pilot study with BRS implanted in coronary bifurcations were included for analysis. All patients underwent OCT of the target vessel after BRS implantation in the main vessel. Coronary 3D reconstruction was performed creating two geometrical models: one was angiography model and the other was OCT model with the implanted BRS. CFD analysis was performed separately on these two models. The main vessel was divided into portions of 0.15 mm length and 0.15mm arc width for point-perpoint comparison of SS between the two models. Reconstruction of the implanted BRS in naturally bent shape was successful in all cases. SS was compared in the matched 205463 portions of the two models. The divergence of shear stress was higher in the OCT model (mean±SD: 2.27 ± 3.95 Pa, maximum: 142.48 Pa) than that in the angiography model (mean±SD: 2.05 ± 3.12 Pa, maximum: 83.63 Pa). Pd/Pa values were lower in the OCT model than in the angiography model for both main vessels and side branches (mean±SD: 0.979 ± 0.009 versus 0.984 ± 0.011, and 0.951 ± 0.068 versus 0.966 ± 0.051). Reconstruction of BRS in naturally bent shape after implantation is feasible. It allows detailed analysis of local flow pattern, including shear stress and Pd/Pa in vivo.
Proc. SPIE. 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling
KEYWORDS: Image visualization, 3D acquisition, Data modeling, Visualization, X-rays, Arteries, 3D modeling, Angiography, X-ray imaging, Image-guided intervention, 3D image processing, Information visualization, Data fusion
Coronary Artery Disease (CAD) results in the buildup of plaque below the intima layer inside the vessel wall of the coronary arteries causing narrowing of the vessel and obstructing blood flow. Percutaneous coronary intervention (PCI) is usually done to enlarge the vessel lumen and regain back normal flow of blood to the heart. During PCI, X-ray imaging is done to assist guide wire movement through the vessels to the area of stenosis. While X-ray imaging allows for good lumen visualization, information on plaque type is unavailable. Also due to the projection nature of the X-ray imaging, additional drawbacks such as foreshortening and overlap of vessels limit the efficacy of the cardiac intervention. Reconstruction of 3D vessel geometry from biplane X-ray acquisitions helps to overcome some of these projection drawbacks. However, the plaque type information remains an issue. In contrast, imaging using computed tomography angiography (CTA) can provide us with information on both lumen and plaque type and allows us to generate a complete 3D coronary vessel tree unaffected by the foreshortening and overlap problems of the X-ray imaging. In this paper, we combine x-ray biplane images with CT angiography to visualize three plaque types (dense calcium, fibrous fatty and necrotic core) on x-ray images. 3D registration using three different registration methods is done between coronary centerlines available from x-ray images and from the CTA volume along with 3D plaque information available from CTA. We compare the different registration methods and evaluate their performance based on 3D root mean squared errors. Two methods are used to project this 3D information onto 2D plane of the x-ray biplane images. Validation of our approach is performed using artificial biplane x-ray datasets.
The inhibitory interaction has long been observed in the lateral eye of the Limulus and been integrated into mechanism
of enhancing contrast. When applying to the enhancement of low-contrast image for segmenting interested objects, the
original lateral inhibition model will simultaneously amplify noises while enhancing edges contrast. This paper presents
a new lateral inhibition model, which is called Stick-Guided Lateral Inhibition, for enhancement of low-contrast image
so that week edges may exert a stronger force to catch the boundary of targets in the latter segmentation. First, the guided
inhibition term is introduced as a general framework for improving the performance of lateral inhibition models in the
presence of noises. Then, by using asymmetric sticks to guide the inhibiting process, we are able to accentuate the
intensity gradients of image-edges and contours while suppressing the amplification of noises. Experiments on synthetic
images and remote sensor images show that our model significantly enhances low-contrast images and improves the
performance of latter segmentation.