Short communicationVolume transition analysis: A new approach to resolve reclassification of brain tissue in repeated MRI scans

- Brain tissue segmentations of repeated cerebral MRI scans are compared.
- A new approach to resolve tissue type reclassifications is introduced.
- Voxel inflows from and outflows towards adjacent tissue volumes are quantified.
- Three scan-rescan scenarios imitate data basis of various applications.
- Monodirectional net flows increase with longer timespan and scanner switch.

BackgroundVariability 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 methodWe 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.ResultsIn 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 methodsUnlike 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.ConclusionsThe 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.