Patient-bounded Extrapolation for 3D Region of Interest Reconstruction in C-arm CT

Three-dimensional (3D) region of interest (ROI) imaging with C-arm systems provides anatomical information in a predefined 3D target region at a considerably low X-ray dose. A necessary initial step prior to a 3D acquisition is to isocenter the patient with respect to the target to be scanned. To this end, two low-dose fluoroscopic X-ray acquisitions are usually applied from anterior-posterior (AP) and medio-lateral (ML) views. In this paper, we present a patient-bounded extrapolation method that makes use of these non-collimated fluoroscopic images to improve image quality in 3D ROI reconstruction. The algorithm first extracts the 2D patient contours from the AP and ML images. These 2D contours are then combined to estimate a volumetric model of the patient. Forward-projecting the shape of the model at the eventually acquired C-arm rotation views gives the patient boundary information in the projection domain. In this manner, we are in the position to substantially improve image quality by enforcing the extrapolated line profiles to end at the known patient boundaries, derived from the 3D shape model estimate. The proposed method is evaluated on five clinical datasets with different degrees of truncation. The proposed algorithm achieved a relative root mean square error (rRMSE) of 0.7% with respect to non-truncated data, even in the presence of severe truncation, compared to 8.7% from a state-of-the-art heuristic extrapolation.