Subject-Matched Templates for Spatial Normalization

  • Authors:

  • Torsten Rohlfing;Edith V. Sullivan;Adolf Pfefferbaum

  • Affiliations:

  • Neuroscience Program, SRI International, Menlo Park, USA;Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, USA;Neuroscience Program, SRI International, Menlo Park, USA and Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, USA

  • Venue:
  • MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
  • Year:
  • 2009

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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.