Patient-Specific Cerebral Vessel Segmentation with Application in Hemodynamic Simulation
Cerebral 3-D rotational angiography has become the state-of-the-art imaging modality in modern angio suites for diagnosis and treatment planning of cerebrovascular diseases, e. g. intracranial aneurysms. Among other reasons, it is believed that the incidence of aneurysms is due to the local prevalent hemodynamic pattern. To study such a hemodynamic behavior, the 3-D vessel geometry has to be extracted from 3-D DSA data. Since 3-D DSA data may be influenced by beam hardening, inhomogeneous contrast agent distribution, patient movement or the applied reconstruction kernel, this thesis describes a novel vessel segmentation framework seamlessly combining 2-D and 3-D vessel information to overcome the aforementioned factors of influence. The main purpose of this framework is to validate 3-D segmentation results based on 2-D information and to increase the accuracy of 3-D vessel geometries by incorporating additional 2-D vessel information into the 3-D segmentation process. Three major algorithmic contributions are given within this framework: (1) a classification-based summation algorithm of 2-D DSA series such that 2-D vessel segmentation becomes feasible, (2) a 3-D ellipsoid-based vessel segmentation method which allows for local adaptations driven by 2-D vessel segmentations and (3) a mesh size evaluation study investigating the influence of different mesh type elements and resolutions w. r. t. hemodynamic simulation results. Moreover, this work is chamfered by a simulation study which evaluates the impact of different vessel geometries on the simulation result. The vessel geometries are computed by different segmentation techniques working on the same patient dataset. The evaluation of each framework component revealed high accuracy and algorithmic stability to be applied in a clinical environment.