Artifact Reduction in Spare-view Image Reconstruction in C-arm CT

C-arm CT images suffer often from data truncation artifacts as a result of the limited detector size and also of interventional devices that are outside of the imaging field-of-view (FOV) of the C-arm CBCT. The artifacts appear prominently in the form of streaks especially when the scanning angular sampling is sparse. In this work, we investigate optimization-based image reconstruction from sparse-view, angularly-varying truncated data with reduced artifacts. The reconstruction problem is formulated as a constrained optimization program in which a combination fidelity term of data and data-derivative was used for effective suppression of the truncation artifacts, and the Chambolle-Pock (CP) algorithm was used to solve the optimization program. The results of the study suggest that while the streak artifacts caused by the angularly-varying truncation in FDK reconstruction become severe as the number of view decreases, the image quality yielded by the optimization-based reconstruction remains largely unchanged.