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.