Open AccessComparison of structural MRI brain measures between 1.5 and 3 T: Data from the Lothian Birth Cohort 1936
Author(s) -
Buchanan Colin R.,
Muñoz Maniega Susana,
Valdés Hernández Maria C.,
Ballerini Lucia,
Barclay Gayle,
Taylor Adele M.,
Russ Tom C.,
TuckerDrob Elliot M.,
Wardlaw Joanna M.,
Deary Ian J.,
Bastin Mark E.,
Cox Simon R.
Publication year - 2021
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.25473
Subject(s) - intraclass correlation , consistency (knowledge bases) , diffusion mri , fractional anisotropy , scanner , white matter , consistency model , psychology , magnetic resonance imaging , statistics , mathematics , nuclear medicine , medicine , computer science , artificial intelligence , algorithm , radiology , psychometrics , correctness
Multi‐scanner MRI studies are reliant on understanding the apparent differences in imaging measures between different scanners. We provide a comprehensive analysis of T 1 ‐weighted and diffusion MRI (dMRI) structural brain measures between a 1.5 T GE Signa Horizon HDx and a 3 T Siemens Magnetom Prisma using 91 community‐dwelling older participants (aged 82 years). Although we found considerable differences in absolute measurements (global tissue volumes were measured as ~6–11% higher and fractional anisotropy [FA] was 33% higher at 3 T than at 1.5 T), between‐scanner consistency was good to excellent for global volumetric and dMRI measures (intraclass correlation coefficient [ICC] range: .612–.993) and fair to good for 68 cortical regions (FreeSurfer) and cortical surface measures (mean ICC: .504–.763). Between‐scanner consistency was fair for dMRI measures of 12 major white matter tracts (mean ICC: .475–.564), and the general factors of these tracts provided excellent consistency (ICC ≥ .769). Whole‐brain structural networks provided good to excellent consistency for global metrics (ICC ≥ .612). Although consistency was poor for individual network connections (mean ICCs: .275−.280), this was driven by a large difference in network sparsity (.599 vs. .334), and consistency was improved when comparing only the connections present in every participant (mean ICCs: .533–.647). Regression‐based k‐fold cross‐validation showed that, particularly for global volumes, between‐scanner differences could be largely eliminated ( R 2 range .615–.991). We conclude that low granularity measures of brain structure can be reliably matched between the scanners tested, but caution is warranted when combining high granularity information from different scanners.