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Lumen segmentation is relevant for quantification of the degree of stenoses and as preprocessing step for the analysis of the vessel wall, and for the simulation and planing of interventions. Although lumen segmentation in CTA has received considerable interest, it remains a challenge for atherosclerotic vessels. In this paper we propose a semi-automatic lumen segmentation method using a levelset approach, which utilizes both boundary information and regional intensity information to accurately segment the lumen of atherosclerotic vessels. The method was trained and evaluated on a publicly available database of 56 carotid arteries. The average Dice similarity coefficient was 88.9 %, the mean absolute surface distance was 0.31 mm. Compared to using only boundary information, this method yields better segmentation in severely atheroslerotic vessels.

In clinical practice, detection and quantification of calcified lesions in the coronary arteries and aorta is normally performed on non contrast enhanced Computed Tomography (CT) scans. This CT scan is generally followed by a contrast enhanced angiography (CTA) scan for better plaque visualization. We propose and evaluate a method for the detection of calcified lesions on CTA scans. Calcium lesion detection is straightforward on a CT scan, however performing the same task on a contrast enhanced angiography (CTA) scan is challenging, as the intensity of the contrast enhanced vessel lumen is very similar to that of calcium. Our method is based on semi-automatic centreline extraction and subsequent segmentation of high intensity objects to detect calcified lesions in the descending aorta. The method is evaluated on 19 datasets, and the results are compared to manual detection of calcifications on the CT scan. The proposed method has an accuracy of 82.4%, and 91% of the calcified lesions were successfully detected on the CTA scans.

A method to obtain motion artifact-free reconstructed images of the coronary arteries is proposed and evaluated. The method relies on the integration of coronary motion estimation in an iterative computed tomography reconstruction technique. Coronary motion fields are derived from a set of coronary centerlines extracted at multiple cardiac phases within the R-R interval. Start and end points are provided by the user in one time-frame only. Corresponding centerline positions are used to determine the motion fields from phase to phase. Finally, dense motion fields are achieved by thin-plate-spline interpolation and are used to perform a motion-corrected iterative reconstruction of a selected region of interest, which results in an effective improvement of the reconstructed image quality.

A reliable density estimate for the position of the coronary arteries in Computed Tomography (CT) data is beneficial for many coronary image processing applications, such as vessel tracking, lumen segmentations, and calcium scoring. This paper presents a method for obtaining an estimate of the coronary artery location in CT and CT angiography (CTA). The proposed method constructs a patient-specific coronary density estimate using CTA atlas registration. The method is evaluated by quantifying the overlap of the obtained density estimate with 24 manually annotated centrelines of the lumen. Furthermore, the method is quantitatively evaluated when applied in automatic calcium scoring of the coronary arteries, which is an important risk predictor of coronary artery disease. The obtained results were compared to manual annotations for 170 CT datasets.

This paper presents a 2-D/3-D registration method for the alignment of cardiac X-ray images to ECG gated CTA data of the coronary arteries. The purpose of our work is to provide visualization of instruments in relation to pre-operative CTA data during interventional cardiology for improved image guidance, especially in complex procedures. The method utilizes the graphical processing unit (GPU) for rendering of digitally reconstructed radiographs (DRRs) from a 3-D CTA-derived coronary model. Using a multi-scale gradient ascent framework, the normalized cross correlation between the simulated and real X-ray image is optimized. Quantitative evaluation is performed by computing the projection error of the vessel centerlines, which was on average 1.34 mm.