Accurate Cone-Beam Image Reconstruction in C-Arm Computed Tomography
The goal of this thesis is the robust implementation of an accurate cone-beam image reconstruction algorithm such that it is able to process real C-arm data from a circle-plus-arc trajectory. This trajectory is complete and especially well suited for C-arm systems, since it can be performed purely by rotating the C-arm around the patient without the need to move the patient table. We observed two major challenges: i) non-ideal acquisition geometry and ii) data truncation. To account for deviations from the ideal description of the data acquisition geometry, we developed a novel calibration procedure for the circle-plus-arc trajectory. For the second problem, we developed two novel truncation correction methods that approximately but effectively handle data truncation problems. For image reconstruction, we adapted the accurate M-line algorithm. In particular, we applied a novel and numerically stable technique to compute the view dependent derivative with respect to the source trajectory parameter and we developed an efficient way to compute the PI-line backprojection intervals via a polygonal weighting mask. We have chosen the M-line algorithm, since it does not presume an ideal description of the data acquisition geometry. We acquired projection data of a physical phantom of a human thorax on a medical C-arm scanner. Reconstructed images exhibit strong cone-beam artifacts along the bones of the spine when applying the conventional Feldkamp algorithm. These results are compared to those obtained with our implementation of the M-line algorithm. As our ultimate goal, we demonstrate that cone-beam artifacts can be completely eliminated by applying the M-line algorithm to a Tuy complete set of data.