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The benefits of skull stripping in the normalization of clinical fMRI data.

Fischmeister FP, Höllinger I, Klinger N, Geissler A, Wurnig MC, Matt E, Rath J, Robinson SD, Trattnig S, Beisteiner R - Neuroimage Clin (2013)

Bottom Line: The optimum procedure has not been conclusively established, and a critical dichotomy is whether to use input data sets which contain skull signal, or whether skull signal should be removed.Brain activation changes related to deskulled/not-deskulled input data are determined in the context of very recently developed (New Segment, Unified Segmentation) and standard normalization approaches.Analysis of structural and functional data demonstrates that skull stripping improves language localization in MNI space - particularly when used in combination with the New Segment normalization technique.

View Article: PubMed Central - PubMed

Affiliation: Study Group Clinical fMRI, Department of Neurology, Medical University of Vienna, Austria ; High Field MR Center, Medical University of Vienna, Austria.

ABSTRACT
Establishing a reliable correspondence between lesioned brains and a template is challenging using current normalization techniques. The optimum procedure has not been conclusively established, and a critical dichotomy is whether to use input data sets which contain skull signal, or whether skull signal should be removed. Here we provide a first investigation into whether clinical fMRI benefits from skull stripping, based on data from a presurgical language localization task. Brain activation changes related to deskulled/not-deskulled input data are determined in the context of very recently developed (New Segment, Unified Segmentation) and standard normalization approaches. Analysis of structural and functional data demonstrates that skull stripping improves language localization in MNI space - particularly when used in combination with the New Segment normalization technique.

No MeSH data available.


Flow chart delineating the normalization steps illustrating the general approach conducted for each of the six normalization pipelines. See text for further details.
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f0015: Flow chart delineating the normalization steps illustrating the general approach conducted for each of the six normalization pipelines. See text for further details.

Mentions: To check for possible differences between skulled and deskulled images introduced by the linear transformations of Step I, we performed two analyses. (1) Comparison of skulled with deskulled T1 images after registration of T1 to the mean EPI. (2) Comparison of skulled with deskulled T1 images after Step I had been completed (i.e. after generation of a uniform starting-point for all 6 normalizations). This was done by calculating DICE similarity indices (Dice, 1945) for the skulled/deskulled T1 images. These provide a direct measure of the structural differences between skulled and deskulled T1 at stages (1) and (2). DICE calculations were performed separately for the 4 different lesion groups and with the approach described below (section “Evaluation of structural differences between normalized and template images”). The comparison of skulled with deskulled T1 images was carried out with the deskulled image serving as the reference and the skulled image as the template.


The benefits of skull stripping in the normalization of clinical fMRI data.

Fischmeister FP, Höllinger I, Klinger N, Geissler A, Wurnig MC, Matt E, Rath J, Robinson SD, Trattnig S, Beisteiner R - Neuroimage Clin (2013)

Flow chart delineating the normalization steps illustrating the general approach conducted for each of the six normalization pipelines. See text for further details.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3814956&req=5

f0015: Flow chart delineating the normalization steps illustrating the general approach conducted for each of the six normalization pipelines. See text for further details.
Mentions: To check for possible differences between skulled and deskulled images introduced by the linear transformations of Step I, we performed two analyses. (1) Comparison of skulled with deskulled T1 images after registration of T1 to the mean EPI. (2) Comparison of skulled with deskulled T1 images after Step I had been completed (i.e. after generation of a uniform starting-point for all 6 normalizations). This was done by calculating DICE similarity indices (Dice, 1945) for the skulled/deskulled T1 images. These provide a direct measure of the structural differences between skulled and deskulled T1 at stages (1) and (2). DICE calculations were performed separately for the 4 different lesion groups and with the approach described below (section “Evaluation of structural differences between normalized and template images”). The comparison of skulled with deskulled T1 images was carried out with the deskulled image serving as the reference and the skulled image as the template.

Bottom Line: The optimum procedure has not been conclusively established, and a critical dichotomy is whether to use input data sets which contain skull signal, or whether skull signal should be removed.Brain activation changes related to deskulled/not-deskulled input data are determined in the context of very recently developed (New Segment, Unified Segmentation) and standard normalization approaches.Analysis of structural and functional data demonstrates that skull stripping improves language localization in MNI space - particularly when used in combination with the New Segment normalization technique.

View Article: PubMed Central - PubMed

Affiliation: Study Group Clinical fMRI, Department of Neurology, Medical University of Vienna, Austria ; High Field MR Center, Medical University of Vienna, Austria.

ABSTRACT
Establishing a reliable correspondence between lesioned brains and a template is challenging using current normalization techniques. The optimum procedure has not been conclusively established, and a critical dichotomy is whether to use input data sets which contain skull signal, or whether skull signal should be removed. Here we provide a first investigation into whether clinical fMRI benefits from skull stripping, based on data from a presurgical language localization task. Brain activation changes related to deskulled/not-deskulled input data are determined in the context of very recently developed (New Segment, Unified Segmentation) and standard normalization approaches. Analysis of structural and functional data demonstrates that skull stripping improves language localization in MNI space - particularly when used in combination with the New Segment normalization technique.

No MeSH data available.