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J Neurosci Methods. 2015 Mar 30;243:78-83. doi: 10.1016/j.jneumeth.2015.01.028. Epub 2015 Feb 18.

Volume transition analysis: a new approach to resolve reclassification of brain tissue in repeated MRI scans.

Author information

1
Institute of Epidemiology and Social Medicine, University of Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany. Electronic address: anja.teuber@uni-muenster.de.
2
Department of Neurology, University Hospital Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany.
3
Department of Clinical Radiology, University Hospital Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany.
4
Institute of Epidemiology and Social Medicine, University of Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany.

Abstract

BACKGROUND:

Variability in brain tissue volumes derived from magnetic resonance images is attributable to various sources. In quantitative comparisons it is therefore crucial to distinguish between biologically and methodically conditioned variance and to take spatial accordance into account.

NEW METHOD:

We introduce volume transition analysis as a method that not only provides details on numerical and spatial accordance of tissue volumes in repeated scans but also on voxel shifts between tissue types. Based on brain tissue probability maps, mono- and bidirectional voxel shifts can be examined by explicitly separating volume transitions into source and target. We apply the approach to a set of subject data from repeated intra-scanner (one week and 30 month interval) as well as inter-scanner measurements.

RESULTS:

In all measurement scenarios, we found similar inter-class transitions of 9.9-15.9% of intracranial volume. The percentage of monodirectional net volume transition however increases from 0.3% in short term intra-scanner to 1.6% in long term intra-scanner and 9.3% in inter-scanner comparisons.

COMPARISON WITH EXISTING METHODS:

Unlike most routinely used variability measures volume transition analysis is able to monitor reclassifications and thus to quantify not only balanced flows but also the amount of monodirectional net flows between tissue classes. The approach is independent from group analysis and can thus be applied in as few as two images.

CONCLUSIONS:

The proposed method is an easily applicable tool that is useful in discovering intra-individual brain changes and assists in separating biological from technical variance in structural brain measures.

KEYWORDS:

Brain volume; Image segmentation; MRI; Reliability

PMID:
25701591
DOI:
10.1016/j.jneumeth.2015.01.028
[Indexed for MEDLINE]

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