Motion Estimation and Compensation for Interventional Cardiovascular Image Reconstruction
The minimal invasive interventional treatment of cardiac diseases is of high importance in the modern society. Catheter-based procedures are becoming increasingly complex and novel tools for planning and guiding the interventions are required. In recent years intraprocedural 3-D imaging has found its way into the clinics. Based on 2-D X-ray images from C-arm systems a 3-D image with high spatial resolution can be computed. Cardiac vessels are small and moving fast and thus pose a problem to standard reconstruction algorithms. In this thesis, the issues of existing approaches are investigated and novel algorithms are developed that mitigate todays problems in terms of image quality, runtime and assumptions on the cardiac motion. One major contribution is the development of an optimized ECG-gated reconstruction algorithm compensating for non-periodic motion. A cost function inspired from iterative reconstruction algorithms is used to assess the reconstruction quality of an analytic reconstruction algorithm. This key concept is utilized to derive a motion estimation algorithm. The efficient and compact problem formulation allows for the first time the application of ECG-gating in case of non-periodic motion patterns which cannot be reconstructed with previous methods. This significant finding is incorporated into a novel B-spline based motion estimation algorithm which can cope with flexible 3-D motions over time and uses all the projection data. It again takes advantage of an analytic reconstruction algorithm to arrive at a highly efficient, well parallelizable and stable algorithm. In the evaluation it is shown that the developed algorithms allow the reconstruction of clinically challenging cases at high image quality in under 10 minutes. Therefore it combines the desirable properties of reconstruction algorithms in the interventional environment which no other algorithm provided before.