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dc.contributor.author
Ganzetti, Marco
dc.contributor.author
Wenderoth, Nicole
dc.contributor.author
Mantini, Dante
dc.date.accessioned
2019-12-06T08:53:44Z
dc.date.available
2017-06-11T21:38:36Z
dc.date.available
2019-12-06T08:53:44Z
dc.date.issued
2015-01
dc.identifier.other
10.1007/s12021-015-9277-2
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/107849
dc.identifier.doi
10.3929/ethz-b-000107849
dc.description.abstract
The correction of intensity non-uniformity (INU) in magnetic resonance (MR) images is extremely important to ensure both within-subject and across-subject reliability. Here we tackled the problem of objectively comparing INU correction techniques for T1-weighted images, which are the most commonly used in structural brain imaging. We focused our investigations on the methods integrated in widely used software packages for MR data analysis: FreeSurfer, BrainVoyager, SPM and FSL. We used simulated data to assess the INU fields reconstructed by those methods for controlled inhomogeneity magnitudes and noise levels. For each method, we evaluated a wide range of input parameters and defined an enhanced configuration associated with best reconstruction performance. By comparing enhanced and default configurations, we found that the former often provide much more accurate results. Accordingly, we used enhanced configurations for a more objective comparison between methods. For different levels of INU magnitude and noise, SPM and FSL, which integrate INU correction with brain segmentation, generally outperformed FreeSurfer and BrainVoyager, whose methods are exclusively dedicated to INU correction. Nonetheless, accurate INU field reconstructions can be obtained with FreeSurfer on images with low noise and with BrainVoyager for slow and smooth inhomogeneity profiles. Our study may prove helpful for an accurate selection of the INU correction method to be used based on the characteristics of actual MR data.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Springer
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Bias field
en_US
dc.subject
Brain structure
en_US
dc.subject
Comparative study
en_US
dc.subject
Intensity non-uniformity
en_US
dc.subject
Magnetic resonance imaging
en_US
dc.title
Quantitative Evaluation of Intensity Inhomogeneity Correction Methods for Structural MR Brain Images
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2015-08-26
ethz.journal.title
Neuroinformatics
ethz.journal.volume
14
en_US
ethz.journal.issue
1
en_US
ethz.pages.start
5
en_US
ethz.pages.end
21
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.event.date
Published online 26 August 2015
ethz.identifier.wos
ethz.identifier.scopus
ethz.identifier.nebis
004573511
ethz.publication.place
Totowa, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::02535 - Institut für Bewegungswiss. und Sport / Institut of Human Movement Sc. and Sport::03963 - Wenderoth, Nicole / Wenderoth, Nicole
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::02535 - Institut für Bewegungswiss. und Sport / Institut of Human Movement Sc. and Sport::03963 - Wenderoth, Nicole / Wenderoth, Nicole
ethz.date.deposited
2017-06-11T21:39:25Z
ethz.source
ECIT
ethz.identifier.importid
imp593653c374da258651
ethz.ecitpid
pub:168600
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-15T09:51:10Z
ethz.rosetta.lastUpdated
2019-12-06T08:54:08Z
ethz.rosetta.versionExported
true
ethz.COinS
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