Combining atlas-based parcellation of regional brain data acquired across scanners at 1.5T and 3.0T field strengths

  • Pfefferbaum A
  • Rohlfing T
  • Rosenbloom M
 et al. 
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Abstract

Longitudinal brain morphometric studies designed for data acquisition at a single MRI field strength can be seriously limited by system replacements from lower to higher field strength. Merging data across field strengths has not been endorsed for a variety of reasons, yet the ability to combine such data would broaden longitudinal investigations. To determine whether structural T1-weighted MRI data acquired across MR field strengths could be merged, parcellations of archival SPGR data acquired in 114 individuals at 1.5. T and at 3.0. T within 3. weeks of each other were compared. The first set of analyses examined 1) the correspondence between regional tissue volumes derived from data collected at 1.5. T and 3.0. T and 2) whether there were systematic differences for which a correction factor could be determined and applied to improve measurement agreement. Comparability of regional volume determination at 1.5. T and 3.0. T was assessed with intraclass correlation (ICC) computed on volumes derived from the automated and unsupervised SRI24 atlas registration and parcellation method. A second set of analyses measured the reliability of the registration and quantification using the same approach on longitudinal data acquired in 69 healthy adults at a single field strength, 1.5. T, at an interval < 2. years. The mainstay of the analyses was based on the SRI24 method; to examine the potential of merging data across field strengths and across image analysis packages, a secondary set of analyses used FreeSurfer instead of the SRI24 method. For both methods, a regression-based linear correction function significantly improved correspondence. The results indicated high correspondence between most selected cortical, subcortical, and CSF-filled spaces; correspondence was lowest in the globus pallidus, a region rich in iron, which in turn has a considerable field-dependent effect on signal intensity. Thus, the application of a regression-based correction function that improved the correspondence in regional volume estimations argues well for the proposition that selected T1-weighted regional anatomical brain data can be reliably combined across 1.5. T and 3.0. T field strengths with the application of an appropriate correction procedure. © 2012 Elsevier Inc.

Author-supplied keywords

  • Atlas-based parcellation
  • Brain
  • Field strength
  • FreeSurfer
  • Image registration
  • Longitudinal
  • MRI
  • SRI24

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