title: Volume transition analysis: a new approach to resolve reclassification of brain tissue in repeated MRI scans.
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.
  
  
  METHODS
  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.
  
  
  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.
authors.affiliation: Institute of Epidemiology and Social Medicine, University of Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany. Electronic address: anja.teuber@uni-muenster.de.
authors.forename: Anja
authors.initials: A
authors.lastname: Teuber
authors.affiliation: Department of Neurology, University Hospital Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany.
authors.forename: Jan-Gerd
authors.initials: JG
authors.lastname: Tenberge
authors.affiliation: Department of Clinical Radiology, University Hospital Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany.
authors.forename: Harald
authors.initials: H
authors.lastname: Kugel
authors.affiliation: Department of Neurology, University Hospital Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Deppe
authors.affiliation: Institute of Epidemiology and Social Medicine, University of Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany.
authors.forename: Klaus
authors.initials: K
authors.lastname: Berger
authors.affiliation: Institute of Epidemiology and Social Medicine, University of Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany.
authors.forename: Heike
authors.initials: H
authors.lastname: Wersching
mesh_terms: D000328:Adult; D000368:Aged; D001921:Brain / Q000033:anatomy & histology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009929:Organ Size
keywords: Brain volume; Image segmentation; MRI; Reliability