Inter-scanner reproducibility of brain volumetry: influence of automated brain segmentation software
Abstract Background: The inter-scanner reproducibility of brain volumetry is important in multi-site neuroimaging studies, where the reliability of automated brain segmentation (ABS) tools plays an important role. This study aimed to evaluate the influence of ABS tools on the consistency and reproducibility of the quantified brain volumetry from different scanners. Methods: We included fifteen healthy volunteers who were scanned with 3D isotropic brain T1-weighted sequence on three different 3.0 Tesla MRI scanners (GE, Siemens and Philips). For each individual, the time span between image acquisitions on different scanners was limited to one hour. All the T1-weighted images were processed with FreeSurfer v6.0, FSL v5.0 and AccuBrain ® with default settings to obtain volumetry of brain tissues (e.g. gray matter) and substructures (e.g. basal ganglia structures) if available. Cofficient of variation (CV) was calculated to test inter-scanner variability in brain volumetry of various structures as quantified by these ABS tools. Results: The mean inter-scanner CV values per brain structure among three MRI scanners ranged from 6.946% to 12.29% (mean, 9.577%) for FreeSurfer, 7.245% to 20.98% (mean, 12.60%) for FSL and 1.348% to 8.800% (mean value, 3.546%) for AccuBrain @ . In addition, AccuBrain ® and FreeSurfer achieved the lowest mean values of region-specific CV between GE and Siemens scanners (from 0.818% to 5.958% for AccuBrain ® , and from 0.903% to 7.977% for FreeSurfer), while FSL-FIRST had the lowest mean values of region-specific CV between GE and Philips scanners (from 2.603% to 16.310%). AccuBrain ® also had the lowest mean values of region-specific CV between Siemens and Philips (from 1.138% to 6.615%). Conclusion: There is a large discrepancy in the inter-scanner reproducibility of brain volumetry when using different processing software. Image acquisition protocols and selection of ABS tool for brain volumetry quantification have impact on the robustness of results in multi-site studies.