Evaluation and automatic correction of metal-implant-induced artifacts in MR-based attenuation correction in whole-body PET/MR imaging

Abstract

The aim of this paper is to describe a new automatic method for compensation of metal-implant-induced segmentation errors in MR-based attenuation maps (MRMaps) and to evaluate the quantitative influence of those artifacts on the reconstructed PET activity concentration. The developed method uses a PET-based delineation of the patient contour to compensate metal-implant-caused signal voids in the MR scan that is segmented for PET attenuation correction. PET emission data of 13 patients with metal implants examined in a Philips Ingenuity PET/MR were reconstructed with the vendor-provided method for attenuation correction (MRMaporig, PETorig) and additionally with a method for attenuation correction (MRMapcor, PETcor) developed by our group. MRMaps produced by both methods were visually inspected for segmentation errors. The segmentation errors in MRMaporig were classified into four classes (L1 and L2 artifacts inside the lung and B1 and B2 artifacts inside the remaining body depending on the assigned attenuation coefficients). The average relative SUV differences () between PETorig and PETcor of all regions showing wrong attenuation coefficients in MRMaporig were calculated. Additionally, relative SUVmean differences (εrel) of tracer accumulations in hot focal structures inside or in the vicinity of these regions were evaluated. MRMaporig showed erroneous attenuation coefficients inside the regions affected by metal artifacts and inside the patients' lung in all 13 cases. In MRMapcor, all regions with metal artifacts, except for the sternum, were filled with the soft-tissue attenuation coefficient and the lung was correctly segmented in all patients. MRMapcor only showed small residual segmentation errors in eight patients. (mean ± standard deviation) were: ( − 56 ± 3)% for B1, ( − 43 ± 4)% for B2, (21 ± 18)% for L1, (120 ± 47)% for L2 regions. εrel (mean ± standard deviation) of hot focal structures were: ( − 52 ± 12)% in B1, ( − 45 ± 13)% in B2, (19 ± 19)% in L1, (51 ± 31)% in L2 regions. Consequently, metal-implant-induced artifacts severely disturb MR-based attenuation correction and SUV quantification in PET/MR. The developed algorithm is able to compensate for these artifacts and improves SUV quantification accuracy distinctly.