Investigation of optimization-based reconstruction for intra-operative neurological imaging

Intra-operative C-arm cone-beam CT (CBCT) provides significant technical and workflow benefits for neurological interventions. Current C-arm CBCT employs FDK-type algorithms for image reconstruction, which require densely sampled projection data. This imaging practice incurs a large amount of radiation dose and prolonged scanning time. Yet, the reconstruction is susceptible to artifacts caused by both geometry and physical factors such as noise. In this work, we investigated and evaluated the adaptation of optimization-based reconstruction to C-arm-CBCT-based neurological imaging from full-and half-view patient data. The adaptation mainly entailed accurate incorporation of C-arm geometry and appropriate selection of algorithm parameters. The results show that optimization-based algorithms can yield images of effectively suppressed noise and shading artifacts, from half of the amount of data acquired in current clinical applications.