Year: 2008
Type: dissertation
Abstract: Angiographic imaging is a widely used monitoring tool for minimally invasive vascular treatment and pathology access.Especially in deforming abdominal areas, the registration of pre-and intraoperative image data is still an unsolved problem, but important in several aspects.In particular, treatment time and radiation exposure to patient and physician can be significantly reduced with the resulting 2D-3D data fusion.The focus of this work is to provide methods for the registration of 2D vascular images acquired by a stationary C-arm to preoperative 3D angiographic Computed Tomography (CT) volumes, in order to improve the workflow of catheterized liver tumor treatments.Fast and robust vessel segmentation techniques are used to prepare the necessary graph data structures for a successful alignment.Here, we introduce restricted correspondence selection and iterative feature space correction to drive the proposed rigid-body algorithms to global and accurate solutions.Moreover, it is shown for the first time that the assignment of natural constraints on vessel structures allows for a successful recovery of a 3D non-rigid transformation despite a single-view scenario.Based on these results, novel volumetric visualization and roadmapping techniques are developed in order to resolve interventional problems of reduced depth perception, blind navigation, and motion blur.
Author: Martin Groher
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Basic Energy Sciences
Award: G426311843
OpenAlex ID: W25799408