Guy Lavi

Single-seeded coronary artery tracking in CT angiography

A new algorithm for rapid and accurate segmentation of the coronary arterial tree in CT angiography data sets is presented. Each artery is fully tracked from the aortic origin to its distal end, following a single touch by the user anywhere along the vessel. A two-level front propagation technique is applied for the tracking of each vessel. An initial bottom-hat filtering, an adaptive threshold and a heuristic credit system are incorporated to overcome the pitfalls characterizing the coronary environment like the adjacent veins, the proximate chambers and the changing gray level along the artery. The aortic origin and the distal end of each vessel are determined automatically based on specific stopping criteria. An additional seed placed at the aortic root is used to complete the extraction of the entire coronary tree for an overall examination. The algorithm used for the segmentation of the aortic root region is a planar front propagation based on watershed segmentation and an adaptive erosion technique that are needed to avoid leakage into the atria in cases of artifacts or poor image quality. The results of the proposed methodology were evaluated in 34 patients. Artery tracking was proved to be highly robust for images of medium and high quality.

Fast automatic delineation of cardiac volume of interest in MSCT images

Computed Tomography Angiography (CTA) is an emerging modality for assessing cardiac anatomy. The delineation of the cardiac volume of interest (VOI) is a pre-processing step for subsequent visualization or image processing. It serves the suppression of anatomic structures being not in the primary focus of the cardiac application, such as sternum, ribs, spinal column, descending aorta and pulmonary vasculature. These structures obliterate standard visualizations such as direct volume renderings or maximum intensity projections. In addition, outcome and performance of post-processing steps such as ventricle suppression, coronary artery segmentation or the detection of short and long axes of the heart can be improved. The structures being part of the cardiac VOI (coronary arteries and veins, myocardium, ventricles and atria) differ tremendously in appearance. In addition, there is no clear image feature associated with the contour (or better cut-surface) distinguishing between cardiac VOI and surrounding tissue making the automatic delineation of the cardiac VOI a difficult task. The presented approach locates in a first step chest wall and descending aorta in all image slices giving a rough estimate of the location of the heart. In a second step, a Fourier based active contour approach delineates slice-wise the border of the cardiac VOI. The algorithm has been evaluated on 41 multi-slice CT data-sets including cases with coronary stents and venous and arterial bypasses. The typical processing time amounts to 5-10s on a 1GHz P3 PC.

Mapping the coronary arteries on a sphere in CT angiography

Current approaches for coronary artery inspection using cardiac CT angiography scans include curved planar reformation (CPR), slab maximum-intensity projection (MIP) and volume rendering (VR) techniques. While the first two allow a detailed examination of only one vessel or a few segments of the coronary artery tree at a time, the VR techniques are not considered suitable for a thorough clinical assessment. An innovative concept of visualization aimed at revealing the entire coronary tree in a CPR-type environment is presented. The new approach uses a sphere or an ellipsoid as a base surface to map the coronary tree. Using the spherical (or ellipsoidal) coordinate system a “true” surface running through the centerlines of all the vessels is defined. Resampling the volume data with this (preferably thick) surface and using a maximum-intensity projection will produce three possible modes of visualization. In one mode the “true form” surface is texture-mapped with the resampled volume data, while in another the data is projected onto the sphere that served as a base surface, forming the “Globe” mode of visualization. Peeling the data to form a 2D “map” of the entire coronary tree in its context in the heart constitutes the third mode.