Superiorized polyenergetic reconstruction algorithm for reduction of metal artifacts in CT images

Artifacts caused by metal objects such as dental fillings, hip implants, and coronary stents are a significant source of error in many CT scans. These artifacts are caused by numerous factors, including beam hardening, noise, photon starvation, partial volume and exponential edge gradient effects, and scatter. We propose an iterative algorithm for CT image reconstruction which reduces these artifacts. The algorithm does so by (1) accurately modeling polyenergetic X-ray data, (2) statistically weighting the X-ray data to reduce the effect of noisy measurements, and (3) incorporating total variation (TV) as a secondary objective. The recently proposed superiorization methodology provides a solid mathematical foundation for our approach. Our numerical experiments indicate that all three of these features of the algorithm play an important role in reducing metal artifacts.