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Matthias Schneider. Model-Based Respiratory Motion Compensation for Image-Guided Cardiac Interventions. Diplomarbeit (equivalent to master's thsis), University of Erlangen-Nuremberg, Germany, Jan 2010.

In this thesis, we describe and validate a statistical breathing motion model for respiratory motion compensation during image-guided cardiac interventions such as Percutaneous Coronary Interventions (PCI) for Chronic Total Occlusions (CTO). In a preparatory training phase, a preoperative 3-D segmentation of the coronary arteries is automatically registered with a cardiacgated biplane cineangiogram at different breathing phases, and used to build the subject-specific motion model. The trained model mathematically describes the nature of respiratory-induced motion of the heart and is used as prior knowledge for the intraoperative re-registrations of the 3-D roadmap by restricting the search space to the most dominant modes of the motion profile. In this way, the interventionalist is provided visual guiding assistance during cardiac catheterization procedures under live fluoroscopy. The model-constrained registration increases the robustness and accuracy of the dynamic re-registrations, especially for weak data constraints such as low signal-to-noise ratio (SNR), the lack of contrast information in fluoroscopic images, or an intraoperative monoplane setting. This allows for reducing radiation dose without compromising on registration accuracy. Synthetic data as well as phantom and clinical data sets have been used to validate the model-based registration in terms of accuracy, robustness and speed. We were able to significantly accelerate the intraoperative registration with an average 3-D error of less than 2mm even for monoplane settings and tracked guidewire data in the absence of contrast agent, which makes respiratory motion correction feasible during clinical procedures. Moreover, we propose a new methodology for global vessel segmentation in 2-D X-ray images combining a per-pixel probability map from a local vessel enhancement filter with local directional information. The global segmentation provides additional connectivity and geometric shape information, which allows for sophisticated postprocessing techniques in order to refine the segmentation with respect to the requirements of the particular application. We present different postprocessing techniques for hierarchical segmentation, centerline extraction, and catheter removal. We further demonstrate that the global segmentation approach yields better segmentation results and is more robust to noise compared to two conventional local Hessian-based segmentation approaches.
@MastersThesis{Schneider2010b, title = {Model-Based Respiratory Motion Compensation for Image-Guided Cardiac Interventions}, author = {Schneider, Matthias}, school = {University of Erlangen-Nuremberg, Germany}, year = {2010}, month = {Jan}, type = {Diplomarbeit (equivalent to master's thsis)}, abstract = {In this thesis, we describe and validate a statistical breathing motion model for respiratory motion compensation during image-guided cardiac interventions such as Percutaneous Coronary Interventions (PCI) for Chronic Total Occlusions (CTO). In a preparatory training phase, a preoperative 3-D segmentation of the coronary arteries is automatically registered with a cardiacgated biplane cineangiogram at different breathing phases, and used to build the subject-specific motion model. The trained model mathematically describes the nature of respiratory-induced motion of the heart and is used as prior knowledge for the intraoperative re-registrations of the 3-D roadmap by restricting the search space to the most dominant modes of the motion profile. In this way, the interventionalist is provided visual guiding assistance during cardiac catheterization procedures under live fluoroscopy. The model-constrained registration increases the robustness and accuracy of the dynamic re-registrations, especially for weak data constraints such as low signal-to-noise ratio (SNR), the lack of contrast information in fluoroscopic images, or an intraoperative monoplane setting. This allows for reducing radiation dose without compromising on registration accuracy. Synthetic data as well as phantom and clinical data sets have been used to validate the model-based registration in terms of accuracy, robustness and speed. We were able to significantly accelerate the intraoperative registration with an average 3-D error of less than 2mm even for monoplane settings and tracked guidewire data in the absence of contrast agent, which makes respiratory motion correction feasible during clinical procedures. Moreover, we propose a new methodology for global vessel segmentation in 2-D X-ray images combining a per-pixel probability map from a local vessel enhancement filter with local directional information. The global segmentation provides additional connectivity and geometric shape information, which allows for sophisticated postprocessing techniques in order to refine the segmentation with respect to the requirements of the particular application. We present different postprocessing techniques for hierarchical segmentation, centerline extraction, and catheter removal. We further demonstrate that the global segmentation approach yields better segmentation results and is more robust to noise compared to two conventional local Hessian-based segmentation approaches.} }