Doctoral Theses at NTNU

Härtill 8 uppsatser PhD i informasjons- og kommunikasjonsteknologi Rådssalen, Hovedbygget, Trondheim, Norway, Cardiac ultrasound, or echocardiography, is considered to be the quickest and most cost effective imaging modality for assessment of cardiac function. The modality is unique in that it allows for real-time imaging of the heart, using portable equipment. The latest generation of echocardiography scanners are capable of acquiring dense image volumes in either real time, or over a few heart beats. Methods for analyzing these images, however, are lagging behind, with all existing methods for segmentation of 3D ultrasound data considered too computationally intensive to operate at acquisition rate. Availability of methods for analysis of 3D ultrasound in real-time could open up possibilities for very quick and simple measurement of cardiac function, potentially conducted during image acquisition. Results from automatic methods would also lessen the amount of inter- or intra-examiner differences introduced during the analysis, which in turn could lead to more reproducible results. The left ventricle of the heart has traditionally been the chamber of most interest within the field cardiac image analysis. This thesis has therefore focused on the left ventricle, and investigated ways to measure different aspects of the chamber, such as chamber volumes, myocardial strain and myocardial mass, by means of state estimation techniques.The main goal of this thesis has been to explore the possibilities of using state estimation methods for segmentation and tracking of structures in volumetric data with deformable models. The ability of using non-iterative estimators, such as the Kalman filter, for fitting deformable models to image structures would radically reduce the computationally effort required for performing a 3D segmentation, and open up for real-time usage. An existing Kalman tracking framework have therefore been extended to operate in volumetric data. The framework has been successfully demonstrated to fit ellipsoids, spline surfaces, active-shape surfaces and subdivision surfaces to image data. Furthermore, theory for the simultaneous tracking of several models have been developed. Finally, methods for combining edge-detection measurements with speckle-tracking measurements have been shown, with the potential advantages of material tracking with the lack of drift in edge detection.The framework has been demonstrated to successfully conduct high quality