Artifact Reduction From Dental Material in Photon-Counting Detector Computed Tomography Data Sets Based on High-keV Monoenergetic Imaging and Iterative Metal Artifact Reduction Reconstructions-Can We Combine the Best of Two Worlds?

OBJECTIVE

The aim of this study was to compare the effectiveness of common strategies for artifact reduction of dental material in photon-counting detector computed tomography data sets.

MATERIALS AND METHODS

Patients with dental material who underwent clinically indicated CT of the neck were enrolled. Image series were reconstructed using a standard and sharp kernel, with and without iterative metal artifact reduction (IMAR) (Qr40, Qr40 IMAR , Qr60, Qr60 IMAR ) at different virtual monoenergetic imaging (VMI) levels (40-190 keV). On representative slice positions with and without dental artifacts, mean and standard deviation of CT values were measured in all series at identical locations. The mean absolute error of CT values ( ) and the artifact index (AIX) were calculated and analyzed focusing on 3 main comparisons: ( a ) different VMI levels versus 70 keV, ( b ) standard versus sharp kernel, and ( c ) nonuse or use of IMAR reconstruction. The Wilcoxon test was used to assess differences for nonparametric data.

RESULTS

The final cohort comprised 50 patients. Artifact measures decreased for VMI levels >70 keV, yet only significantly so for reconstructions using IMAR (maximum reduction, 25%). The higher image noise of the sharp versus standard kernel is reflected in higher AIX values and is more pronounced in IMAR series (maximum increase, 38%). The most profound artifact reduction was observed for IMAR reconstructions (maximum reduction : 84%; AIX: 90%).

CONCLUSIONS

Metal artifacts caused by large amounts of dental material can be substantially reduced by IMAR, regardless of kernel choice or VMI settings. Increasing the keV level of VMI series, on the other hand, only slightly reduces dental artifacts; this effect, however, is additive to the benefit conferred by IMAR reconstructions.