Avoiding asymmetry-induced bias in longitudinal image processing
Martin Reuter, Bruce Fischl, Martin Reuter, Bruce Fischl
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.
Copyright © 2011 Elsevier Inc. All rights reserved.
Source: PubMed