Inter‐ and intra‐scanner variability of icobrain DM automated brain volumetry software on three different MRI systems. (1st February 2022) - Wittens, Mandy M.J.; Allemeersch, Gert‐Jan; Sima, Diana M.; Naeyaert, Maarten; Vanderhasselt, Tim; Vanbinst, Anne‐Marie; Buls, Nico; De Brucker, Yannick; Raeymaekers, Hubert; Ribbens, Annemie; Smeets, Dirk; Van Hecke, Wim; et al.
Abstract: Background: Magnetic Resonance Imaging (MRI) has become part of clinical routine for diagnosis of neurodegenerative brain disorders. Acquisitions are performed at multiple centers, using multiple imaging systems and is therefore subjected to intra‐ and inter‐scanner variability. Detailed analysis of brain volumetry differences between MRI systems and scan‐rescan acquisitions can provide useful information to correct for different MRI scanner effects in future multi‐center longitudinal studies. Method: The study population (n=10) consisted of five cognitively healthy controls and five patients belonging to different stages of the AD continuum. Subjects underwent brain MRI acquisitions on three different MRI systems with harmonized scan parameters: Philips Achieva dStream 1.5T, Philips Ingenia 3T and GE Discovery MR750w 3T. Sequences were harmonized by keeping acquisition parameters similar (Table 1). To test intra‐scanner variability, each participant underwent two subsequent MRI scans (between‐scan repositioning time 3‐5 minutes) per imaging system, repeated on three different MRI systems within two hours to test inter‐scanner variability. A total of 60 MRI scans were acquired for downstream comparative analysis. Brain volumes, including whole brain, (cortical) gray matter, hippocampal volumes, lateral ventricles, frontal, temporal and parietal cortices were computed with icobrain dm (v5.0). Coefficients of variation (CV), absolute volume differences (AVD), Dice similarity coefficients (DSC) and intraclass correlation coefficients (ICC) were computed to assess within‐ and between‐scanner agreement. Result: The intra‐scanner CVs over all volumes were 0.948% for Discovery MR750w, 1.050% for Achieva and 1.152% for Ingenia. The inter‐scanner CVs were 3.016% for all‐scanner comparisons, 2.044% for Ingenia vs. Achieva, 3.191% for Achieva vs. GE and 3.326% for Ingenia vs. GE. The AVDs and ICCs were in line with the CV values. The intra‐scanner DSCs (mean±SD) over all volumes were 0.917±0.011 for Discovery MR750w, 0.917±0.010 for Achieva and 0.917±0.015 for Ingenia. The inter‐scanner DSCs were 0.898±0.017 for all‐scanner comparisons, 0.912±0.012 for Ingenia vs. Achieva, 0.888±0.013 for Achieva vs. Discovery MR750w and 0.893±0.015 for Ingenia vs. Discovery MR750w (Table 2, 3, Figure 1). Conclusion: The variability results show an excellent agreement (CV<2%) for within‐scanner and a good agreement (CV<5%) for between‐scanner comparisons in terms of clinical relevance, indicating that MRI acquisition harmonization is beneficial for automated MRI volumetry software.