Improving CT-Based PET Attenuation Correction in the Vicinity of Metal Implants by an Iterative Metal Artifact Reduction Algorithm of CT Data and Its Comparison to Dual-Energy-Based Strategies A Phantom Study

Objective: The aim of this study was to evaluate the potential of iterative metal artifact reduction (IMAR) for the improvement of computed tomography (CT)-based position emission tomography (PET) attenuation correction in the vicinity of metal implants and compare it with dual-energy-based metal artifact reduction strategies. Methods: A dedicated dental phantom was constructed consisting of a cylindrical tube filled with [18-F] FDG solution (5300 mL and 50.9 MBq) containing 2 artificial jaws with 1 nonprecious alloy fixed dental prosthesis and 3 single tooth crowns in the lower jaw. Computed tomography measurements of the phantom were acquired on a stand-alone dual-energy CT scanner equipped with IMAR capabilities. A series of 24 CT data sets were obtained using different scan parameters and monoenergetic extrapolation of dual-energy CT acquisitions with and without IMAR reconstruction. Position emission tomography measurements of the phantom were performed on a state-of-art PET/CT scanner. Position emission tomography datawere reconstructed using all 24 previously acquired CT data sets. Relative errors in the quantification of activity concentrations using the different CT scanning and reconstruction parameters were quantified by placement of regions of interest within the phantom. Results: Metal artifacts of different extent were observed in all CT data sets. A marked reduction in CT metal artifacts was observed using IMAR. In general, activity concentrations were overestimated/underestimated in areas of high/low-density metal artifacts, respectively. Relative errors in PET quantification ranged between -71% and +70% without IMAR. Using IMAR, these errors were reduced to a range between -40% and +12%. Averaged absolute values of relative PET quantification errors were 27% and 7% without and with the use of IMAR (P < 0.001), respectively. Iterative metal artifact reduction was superior compared with dual-energy-based metal artifact reduction strategies, and the combination of both strategies did not result in further significant improvement of PET quantification. Conclusions: The use of IMAR in PET/CT is a promising approach for markedly improving image quality and PET quantification in the vicinity of metal implants. Further clinical studies are necessary to assess the clinical performance of this algorithm in patients.