Algorithm-enabled high-performance C-arm cone-beam CT angiography of cerebral vasculature

C-arm CBCT utilizing a flat-panel detector has been implemented in surgical suites for integration of tomographic imaging capabilities in the intra-operative workflow. In particular, cerebral vascular applications have emerged that rely on C-arm CBCT angiography for making clinical decisions on treatment selection. Current C-arm CBCT angiography employing analytic-based reconstruction algorithms has limitations on sub-optimal image quality due to irregular gantry motion, as well as issues of long scanning time and non-negligible imaging dose. In this work, we investigated the adaptation of an optimization-based algorithm for reconstructing C-arm CBCT images of patient cerebral vasculature from full- and reduced-view data. In particular, the gantry-motion irregularity was experimentally characterized and fully incorporated in the reconstruction algorithm. The results show that the optimization-based algorithm can yield visibly improved quality from full-view data, and some important vascular structures of potential clinical utility can be obtained from data containing substantially reduced number of views.