Inter- and Intra-Scanner Variability of Automated Brain Volumetry on Three Magnetic Resonance Imaging Systems in Alzheimer’s Disease and Controls

Abstract

Magnetic Resonance Imaging (MRI) has become part of the clinical routine for diagnosing neurodegenerative disorders. Since acquisitions are performed at multiple centers using multiple imaging systems, detailed analysis of brain volumetry differences between MRI systems and scan-rescan acquisitions can provide valuable information to correct for different MRI scanner effects in multi-center longitudinal studies. To this end, five healthy controls and five patients belonging to various stages of the AD continuum underwent brain MRI acquisitions on three different MRI systems (Philips Achieva dStream 1.5T, Philips Ingenia 3T, and GE Discovery MR750w 3T) with harmonized scan parameters. Each participant underwent two subsequent MRI scans per imaging system, repeated on three different MRI systems within 2 h. Brain volumes computed by icobrain dm (v5.0) were analyzed using absolute and percentual volume differences, Dice similarity (DSC) and intraclass correlation coefficients, and coefficients of variation (CV). Harmonized scans obtained with different scanners of the same manufacturer had a measurement error closer to the intra-scanner performance. The gap between intra- and inter-scanner comparisons grew when comparing scans from different manufacturers. This was observed at image level (image contrast, similarity, and geometry) and translated into a higher variability of automated brain volumetry. Mixed effects modeling revealed a significant effect of scanner type on some brain volumes, and of the scanner combination on DSC. The study concluded a good intra- and inter-scanner reproducibility, as illustrated by an average intra-scanner (inter-scanner) CV below 2% (5%) and an excellent overlap of brain structure segmentation (mean DSC > 0.88).

Validation of cross-sectional and longitudinal ComBat harmonization methods for magnetic resonance imaging data on a travelling subject cohort

“…The scan-rescan variability observed in our study, both within and across scanners, compares well with those previously reported for volumes ( Wittens et al, 2021 ; De Guio et al, 2016 ; Jovicich et al, 2009 ; Kruggel et al, 2010 ; Yang et al, 2016 ; Fujita et al, 2019 ; Vavasour et al, 2019 ; Deprez et al, 2018 ), cortical thickness ( Fujita et al, 2019 ; Kecskemeti et al, 2021 ; Mcguire et al, 2017 ) and diffusion metrics ( Acheson et al, 2017 ; Grech‐Sollars et al, 2015 ; Kamagata et al, 2015 ; Liu et al, 2014 ; Palacios et al, 2017 ; Prohl et al, 2019 ; Shahim et al, 2017 ; Veenith et al, 2013 ; Zhou et al, 2018 ).…”

Brain age as a surrogate marker for cognitive performance in multiple sclerosis

“…Table S1 for data sources), in which all three scanner types that were used in the test datasets were also represented (Philips Achieva, Philips Ingenia and Siemens Verio). Nonetheless, preprocessing of brain images already partly counteracts heterogeneity across scanners, as the ico brain software used to segment the MR images shows limited inter‐scanner variability [40, 41]. Therefore, this bias has been deemed to have been properly addressed.…”

“…Nonetheless, preprocessing of brain images already partly counteracts heterogeneity across scanners, as the icobrain software used to segment the MR images shows limited inter-scanner variability [40,41]. Therefore, this bias has been deemed to have been properly addressed.…”

Longitudinal variability analysis on low-dose abdominal CT with deep learning-based segmentation

“…ICC and CV expressed the similarity level across the observations of scans and the differences between scans, respectively. 17 Our study used ICC and CV to evaluate the difference of mean intensity and area of each same segmented tissue from different scans. ICC was computed using a two-way mixed method:…”