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

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.

Pubmed ID: 22297204

Authors

  • Pfefferbaum A
  • Rohlfing T
  • Rosenbloom MJ
  • Sullivan EV

Journal

NeuroImage

Publication Data

April 2, 2012

Associated Grants

  • Agency: NIAAA NIH HHS, Id: AA005965
  • Agency: NIAAA NIH HHS, Id: AA016273
  • Agency: NIAAA NIH HHS, Id: AA017168
  • Agency: NIA NIH HHS, Id: AG017919
  • Agency: NIBIB NIH HHS, Id: EB008381
  • Agency: NIAAA NIH HHS, Id: K05 AA017168
  • Agency: NIAAA NIH HHS, Id: K05 AA017168-05
  • Agency: NIAAA NIH HHS, Id: R01 AA005965
  • Agency: NIAAA NIH HHS, Id: R01 AA005965-29
  • Agency: NIAAA NIH HHS, Id: R01 AA012388
  • Agency: NIA NIH HHS, Id: R01 AG017919
  • Agency: NIA NIH HHS, Id: R01 AG017919-10
  • Agency: NIBIB NIH HHS, Id: R01 EB008381
  • Agency: NIBIB NIH HHS, Id: R01 EB008381-02
  • Agency: NIAAA NIH HHS, Id: R37 AA010723
  • Agency: NIAAA NIH HHS, Id: R37 AA010723-15

Mesh Terms

  • Adult
  • Brain
  • Brain Mapping
  • Female
  • Humans
  • Magnetic Resonance Imaging
  • Male