A framework for automated coronary artery tracking of low axial resolution multi slice CT images

Abstract

Low axial resolution data such as multi-slice CT(MSCT) used for coronary artery disease screening must balance the potential loss in image clarity, detail and partial volume effects with the benefits to the patient such as faster acquisition time leading to lower dose exposure. In addition, tracking of the coronary arteries can aid the location of objects contained within, thus helping to differentiate them from similar in appearance, difficult to discern neighbouring regions. A fully automated system has been developed to segment and track the main coronary arteries and visualize the results. Automated heart isolation is carried out for each slice of an MSCT image using active contour methods. Ascending aorta and artery root segmentation is performed using a combination of active contours, morphological operators and geometric analysis of coronary anatomy to identify a starting point for vessel tracking. Artery tracking and backtracking employs analysis of vessel position combined with segmented region shape analysis to obtain artery paths. Robust, accurate threshold parameters are calculated for segmentation utilizing Gaussian Mixture Model fitting and analysis. The low axial resolution of our MSCT data sets, in combination with poor image clarity and noise presented the greatest challenge. Classification techniques such as shape analysis have been utilized to good effect and our results to date have shown that such deficiencies in the data can be overcome, further promoting the positive benefits to patients.