A New Iterative Metal Artifact Reduction Algorithm for Both Energy-Integrating and Photon-Counting CT Systems

Abstract

Objectives The aim of this study was to introduce and evaluate a new metal artifact reduction framework (iMARv2) that addresses the drawbacks (residual artifacts after correction and user preferences for image quality) associated with the current clinically applied iMAR. Materials and Methods A new iMARv2 has been introduced, combining the current iMAR with new modular components to remove residual metal artifacts after image correction. The postcorrection image impression is adjustable with user-selectable strength settings. Phantom scans from an energy-integrating and a photon-counting detector CT were used to assess image quality, including a Gammex phantom and anthropomorphic phantoms. In addition, 36 clinical cases (with metallic implants such as dental fillings, hip replacements, and spinal screws) were reconstructed and evaluated in a blinded and randomized reader study. Results The Gammex phantom showed lower HU errors compared with the uncorrected image at almost all iMAR and iMARv2 settings evaluated, with only minor differences between iMAR and the different iMARv2 settings. In addition, the anthropomorphic phantoms showed a trend toward lower errors with higher iMARv2 strength settings. On average, the iMARv2 strength 3 performed best of all the clinical reconstructions evaluated, with a significant increase in diagnostic confidence and decrease in artifacts. All hip and dental cases showed a significant increase in diagnostic confidence and decrease in artifact strength, and the improvements from iMARv2 in the dental cases were significant compared with iMAR. There were no significant improvements in the spine. Conclusions This work has introduced and evaluated a new method for metal artifact reduction and demonstrated its utility in routine clinical datasets. The greatest improvements were seen in dental fillings, where iMARv2 significantly improved image quality compared with conventional iMAR.