Within-Subject Template Estimation for Unbiased Longitudinal Image Analysis

Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA.
NeuroImage (Impact Factor: 6.36). 03/2012; 61(4):1402-18. DOI: 10.1016/j.neuroimage.2012.02.084
Source: PubMed


Longitudinal image analysis has become increasingly important in clinical studies of normal aging and neurodegenerative disorders. Furthermore, there is a growing appreciation of the potential utility of longitudinally acquired structural images and reliable image processing to evaluate disease modifying therapies. Challenges 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, based on unbiased, robust, within-subject template creation, for automatic surface reconstruction and segmentation of brain MRI of arbitrarily many time points. We demonstrate that it is essential to treat all input images exactly the same as removing only interpolation asymmetries is not sufficient to remove processing bias. We successfully reduce variability and avoid over-regularization by initializing the processing in each time point with common information from the subject template. 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.

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Available from: Nick Schmansky, Jan 06, 2014
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    • "Anatomic segmentation and cortical thickness measurements were performed using FreeSurfer's longitudinal processing stream (v5.3; Desikan-Killiany atlas for surface parcellation and probabilistic atlas for subcortical segmentation) (Reuter et al., 2012). In order to ensure accuracy, FreeSurfer's output was reviewed, manually corrected and rerun as needed by an experienced MRI postprocessor. "
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    • "FreeSurfer morpho - metric procedures have been demonstrated to show good test – retest reliability across scanner manufacturers and across field strengths ( Han et al . , 2006 ; Reuter , Schmansk , Rosas , & Fischl , 2012 ) . Cortical surface area ( pial area ) and thickness values were automatically extracted for left and right hemispheres by the FreeSurfer software . "
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    Laterality 10/2015; DOI:10.1080/1357650X.2015.1096939 · 1.13 Impact Factor
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    • "Briefly, T1-weighted 3D images were preprocessed with intensity variations correction, normalization, affine registration to the Talairach atlas, skull stripping, and segmentation of grey and white matter. The pipeline for longitudinal processing has been used that includes the creation of an unbiased within-subject template using robust, inverse consistent registration [9]. For cortical thickness , we used surface-based analysis of thickness values at each vertex. "
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