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Voxel-based cortical thickness measurements in MRI

Open access
Hutton, Chloe, De Vita, Enrico, Ashburner, John, Deichmann, Ralf, Turner, Robert·2008·NeuroImage

Abstract

The thickness of the cerebral cortex can provide valuable information about normal and abnormal neuroanatomy. High resolution MRI together with powerful image processing techniques has made it possible to perform these measurements automatically over the whole brain. Here we present a method for automatically generating voxel-based cortical thickness (VBCT) maps. This technique results in maps where each voxel in the grey matter is assigned a thickness value. Sub-voxel measurements of thickness are possible using sub-sampling and interpolation of the image information. The method is applied to repeated MRI scans of a single subject from two MRI scanners to demonstrate its robustness and reproducibility. A simulated data set is used to show that small focal differences in thickness between two groups of subjects can be detected. We propose that the analysis of VBCT maps can provide results that are complementary to other anatomical analyses such as voxel-based morphometry.

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Crossref · 10.1016/j.neuroimage.2008.01.027

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Metadata

Authors
Hutton, C., De Vita, E., Ashburner, J., Deichmann, R., & Turner, R.
Venue
NeuroImage
Publisher
Elsevier BV
Published
2008-05
Volume
40
Issue
4
Pages
1701-1710
ISSN
1053-8119 (ISSN-L)
OpenAlex
W2029262131
Cited by
209
References
6
FWCI
11.95
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Voxel-based cortical thickness measurements in MRI. https://app.cochat.ai/papers/10.1016/j.neuroimage.2008.01.027
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Hutton, C., De Vita, E., Ashburner, J., Deichmann, R., & Turner, R. (2008). Voxel-based cortical thickness measurements in MRI. NeuroImage. https://doi.org/10.1016/j.neuroimage.2008.01.027

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.