Characterization of spectral X-ray imaging for dental cone-beam computed tomography

The recent advancement in detector technology contributed towards the development of photon counting detectors with the ability to discriminate photons according to their energy on reaching the detector. This provides spectral information about the acquired object; thus, giving additional data on the type of material as well as its density. In this paper, we investigate possible reduction of dental artifacts in cone-beam CT (CBCT) via integration of spectral information into a penalized maximum log-likelihood algorithm. For this investigation we simulated (with Monte-Carlo CT simulator) a virtual jaw phantom, which replicates components of a real jaw such as soft-tissue, bone, teeth and gold crowns. A maximum-likelihood basis-component decomposition technique was used to calculate sinograms of the individual materials. The decomposition revealed the spatial as well as material density of the dental implant. This information was passed on as prior information into the penalized maximum log-likelihood algorithm. The resulting reconstructions showed significant reduced streaking artifacts. The overall image quality is improved such that the contrast-to-noise ratio increased compared to the conventional FBP reconstruction. In this work we presented a new algorithm that makes use of spectral information to provide a prior for a penalized maximum log-likelihood algorithm.