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Intracranial volume in neuroimaging: estimation and use in regional brain volume normalization

Doctoral thesis
Authors Niklas Klasson
Date of public defense 2019-03-15
ISBN 978-91-7833-305-9
Publisher Göteborgs universitet
Publication year 2019
Published at Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry
Language en
Keywords magnetic resonance imaging, intracranial volume, normalization
Subject categories Radiology, Nuclear Medicine and Medical Imaging, Medical Image Processing


The aim of this thesis is to validate methods for estimation of intracranial volume in magnetic resonance images and to improve our understanding of the effect of intracranial volume normalization. To achieve the first part of the aim, 62 gold standard estimates of intracranial volume were generated by manually segmenting 1.5 T T1-weighted magnetic resonance images. These estimates were then used to validate a more work-efficient manual method that is frequently used in neuroimaging research. We also proposed an even more work-efficient method for situations where only a strong linear association between estimate and gold standard are required (rather than a strong agreement). Finally, we evaluated the validity of a frequently used automatic method for estimation of intracranial volume. To achieve the second part of the aim, we presented mathematical functions that predict the effect of intracranial volume normalization on the mean value and variance of the brain estimates and their Pearson’s correlation to intracranial volume. We found that segmentations of one intracranial area every 10th mm in magnetic resonance images will result in valid estimates of intracranial volume (intraclass correlation with absolute agreement to gold standard estimates >0.998). The segmentation of two intracranial areas and the estimation of the perpendicular intracranial width will result in estimates with strong linear association to gold standard estimates (Pearson’s correlation >0.99). It was also shown that FreeSurfer’s automatic estimates of intracranial volume risk being biased by total brain volume. Further, the presented mathematical functions closely predicted the effect of intracranial volume normalization on certain statistics of brain estimates, both in a simulation and compared to actual data from other studies. All these findings contribute to an improved intracranial volume estimation and a better use of intracranial volume in regional brain volume normalization.

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