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LABORATORY FOR COMPUTATIONAL LONGITUDINAL NEUROIMAGING (LCLN)

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reuter-long12
Within-Subject Template Estimation for Unbiased Longitudinal Image Analysis. M.Reuter, N.J.Schmansky, H.D.Rosas, B.Fischl. NeuroImage 61(4):1402-1418, 2012. [PDF] [BibTex] [DOI-Link] [PubMed] details

Challenges in longitudinal image analysis have been related to the variability that is inherent in the available cross-sectional processing tools, to the introduction of bias in longitudinal processing and to potential over-regularization. In this paper we introduce a novel longitudinal image processing framework for FreeSurfer, based on unbiased, robust, within-subject template creation, for automatic surface reconstruction and segmentation of brain MRI of arbitrarily many time points. The presented results show a significant increase in precision and discrimination power while preserving the ability to detect large anatomical deviations; as such they hold great potential in clinical applications, e.g. allowing for smaller sample sizes or shorter trials to establish disease specific biomarkers or to quantify drug effects. hide

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Martin Reuter - MIT - Cambridge, MA, USA - EMail: reu...@mit.edu
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