Carotid vasculature modeling from patient CT angiography studies for interventional procedures simulation

Objective: A practical method for patient-specific modeling of the aortic arch and the entire carotid vasculature from computed tomography angiography (CTA) scans for morphologic analysis and for interventional procedure simulation. Materials and methods: The method starts with the automatic watershed-based segmentation of the aorta and the construction of an a-priori intensity probability distribution function for arteries. The carotid arteries are then segmented with a graph min-cut method based on a new edge weighting function that adaptively couples voxel intensity, intensity prior, and local vesselness shape prior. Finally, the same graph-cut optimization framework is used to interactively remove a few unwanted veins segments and to fill in minor vessel discontinuities caused by intensity variations. Results: We validate our modeling method with two experimental studies on 71 multicenter clinical CTA datasets, including carotid bifurcation lumen segmentation on 56 CTAs from the MICCAI'2009 3D Segmentation Challenge. Segmentation results show that our method is comparable to the best existing methods and was successful in modeling the entire carotid vasculature with a Dice similarity measure of 84.5% (SD = 3.3%) and MSSD 0.48 mm (SD = 0.12 mm.) Simulation study shows that patient-specific simulations with four patient-specific models generated by our segmentation method on the ANGIO Mentor^TM simulator platform are robust, realistic, and greatly improve the simulation. Conclusion: This constitutes a proof-of-concept that patient-specific CTA-based modeling and simulation of carotid interventional procedures are practical in a clinical environment.