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PMID: 18063391 Published · ppublish English Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils.

NeuroImage ·Vol. 39 ·No. 4 ·2008-02-15 ·Pages 1752-62

Boyes RG, Gunter JL, Frost C, Janke AL, Yeatman T, Hill DL, Bernstein MA, Thompson PM, Weiner MW, Schuff N, Alexander GE, Killiany RJ, DeCarli C, Jack CR, Fox NC, ADNI Study

Abstract

Measures of structural brain change based on longitudinal MR imaging are increasingly important but can be degraded by intensity non-uniformity. This non-uniformity can be more pronounced at higher field strengths, or when using multichannel receiver coils. We assessed the ability of the non-parametric non-uniform intensity normalization (N3) technique to correct non-uniformity in 72 volumetric brain MR scans from the preparatory phase of the Alzheimer's Disease Neuroimaging Initiative (ADNI). Normal elderly subjects (n=18) were scanned on different 3-T scanners with a multichannel phased array receiver coil at baseline, using magnetization prepared rapid gradient echo (MP-RAGE) and spoiled gradient echo (SPGR) pulse sequences, and again 2 weeks later. When applying N3, we used five brain masks of varying accuracy and four spline smoothing distances (d=50, 100, 150 and 200 mm) to ascertain which combination of parameters optimally reduces the non-uniformity. We used the normalized white matter intensity variance (standard deviation/mean) to ascertain quantitatively the correction for a single scan; we used the variance of the normalized difference image to assess quantitatively the consistency of the correction over time from registered scan pairs. Our results showed statistically significant (p<0.01) improvement in uniformity for individual scans and reduction in the normalized difference image variance when using masks that identified distinct brain tissue classes, and when using smaller spline smoothing distances (e.g., 50-100 mm) for both MP-RAGE and SPGR pulse sequences. These optimized settings may assist future large-scale studies where 3-T scanners and phased array receiver coils are used, such as ADNI, so that intensity non-uniformity does not influence the power of MR imaging to detect disease progression and the factors that influence it.

MeSH Terms
Aged Algorithms Alzheimer Disease/pathology Brain/pathology Calibration Cognition Disorders/pathology Data Interpretation, Statistical Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging/instrumentation Reproducibility of Results
Authors & Affiliations
16 authors, click to expand affiliations / ORCID
Boyes Richard G
Dementia Research Centre, Institute of Neurology, Box 16, University College London, Queen Square, London, UK. [email protected]
Gunter Jeff L
Frost Chris
Janke Andrew L
Yeatman Thomas
Hill Derek L G
Bernstein Matt A
Thompson Paul M
Weiner Michael W
Schuff Norbert
Alexander Gene E
Killiany Ronald J
DeCarli Charles
Jack Clifford R
Fox Nick C
ADNI Study
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Article Info
Journal
NeuroImage
Abbr.
Neuroimage
ISSN
1053-8119
Published
2008-02-15
Epub
2007-00-30
Pages
1752-62
Language
English
Region
United States
NLM ID
9215515
PMCID
PMC2562663
Subset
IM
Grants
NIA NIH HHS · U01 AG 024904 · United States
NIA NIH HHS · U01 AG024904-03 · United States
NIA NIH HHS · P01 AG019724 · United States
NIA NIH HHS · P01 AG019724-050002 · United States
Medical Research Council · G0601846 · United Kingdom
NIA NIH HHS · P30 AG010129-17 · United States
NIA NIH HHS · P30 AG010129 · United States
NIA NIH HHS · U01 AG024904 · United States
NIA NIH HHS · U19 AG010483 · United States
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