Impact of MRI-based Segmentation Artifacts on Amyloid- and FDG-PET Quantitation

Magnet resonance image (MRI)-based segmentations are widely used for clinical brain research, especially in conjunction with positron-emission-tomography (PET). Although artifacts due to segmentation errors arise commonly, the impact of these artifacts on PET quantitation has not yet been investigated systematically. Therefore, the aim of this study was to assess the effect of segmentation errors on [(18)F]-AV45 and [(18)F]-FDG PET quantitation, with and without correction for partial volume effects (PVE).

119 subjects with both [(18)F]-AV45, and [(18)F]-FDG PET as well as T1-weighted MRI at baseline and at two-year follow-up were selected from the ADNI cohort, and their MRI brain images were segmented using PMOD 3.5. MRIs with segmentation artifacts were masked with the corresponding [(18)F]-FDG PET standard-uptake-value (SUV) images to elucidate and quantify the impact of artifacts on PET analyses for six defined volumes-of-interest (VOI). Artifact volumes were calculated for each VOI, together with error-[%] and root-mean-square-errors (RMSE) in uncorrected and PVE corrected SUV results for the two PET tracers. We also assessed the bias in longitudinal PET data.

Artifacts occurred most frequently in the parietal cortex VOI. For [(18)F]-AV45 and [(18)F]-FDG PET, the percentage-errors were dependent on artifact volumes. PVEC SUVs were consequently more distorted than were their uncorrected counterparts. In static and longitudinal assessment, a small subgroup of subjects with large artifacts (≥1500 voxels; ≙5.06 cm³) accounted for much of the PET quantitation bias.

Large segmentation artifacts need to be detected and resolved as they considerably bias PET quantitation, especially when PVEC is applied to PET data.