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Subject-Matched Templates for Spatial Normalization

  • Torsten Rohlfing
  • Edith V. Sullivan
  • Adolf Pfefferbaum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Torsten Rohlfing
    • 1
  • Edith V. Sullivan
    • 2
  • Adolf Pfefferbaum
    • 1
    • 2
  1. 1.Neuroscience ProgramSRI InternationalMenlo ParkUSA
  2. 2.Department of Psychiatry and Behavioral SciencesStanford UniversityStanfordUSA

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