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Avoiding asymmetry-induced bias in longitudinal image processing

Authors
Journal
NeuroImage
1053-8119
Publisher
Elsevier
Publication Date
Volume
57
Issue
1
Identifiers
DOI: 10.1016/j.neuroimage.2011.02.076

Abstract

Abstract Longitudinal image processing procedures frequently transfer or pool information across time within subject, with the dual goals of reducing the variability and increasing the accuracy of the derived measures. In this note, we discuss common difficulties in longitudinal image processing, focusing on the introduction of bias, and describe the approaches we have taken to avoid them in the FreeSurfer longitudinal processing stream.

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