Paper Info
Title
Subject-matched templates for spatial normalization.
Abstract
Spatial normalization of images from multiple subjects is a common problem in group comparison studies, such as voxel-based and deformation-based morphometric analyses. Use of a study-specific template for normalization may improve normalization accuracy over a study-independent standard template (Good et al., NeuroImage, 14(1):21-36, 2001). Here, we develop this approach further by introducing the concept of subject-matched templates. Rather than using a single template for the entire population, a different template is used for every subject, with the template matched to the subject in terms of age, sex, and potentially other parameters (e.g., disease). All subject-matched templates are created from a single generative regression model of atlas appearance, thus providing a priori template-to-template correspondence without registration. We demonstrate that such an approach is technically feasible and significantly improves spatial normalization accuracy over using a single template.
Year
DOI
Venue
2009
10.1007/978-3-642-04271-3_28
MICCAI
Keywords
Field
DocType
spatial normalization,multiple subject,single template,single generative regression model,different template,subject-matched templates,spatial normalization accuracy,study-specific template,normalization accuracy,study-independent standard template,subject-matched template,computer simulation,regression model,template matching,algorithms,young adult,aging,magnetic resonance imaging
Computer vision,Reference values,Population,Normalization (statistics),Pattern recognition,Regression analysis,Computer science,A priori and a posteriori,Spatial normalization,Artificial intelligence,Template
Conference
Volume
Issue
ISSN
12
Pt 2
0302-9743
Citations 
PageRank 
References 
3
0.42
9
Authors
3
Name
Order
Citations
PageRank
Torsten Rohlfing148633.44
Edith V Sullivan215019.25
Adolf Pfefferbaum317420.61