Multivariate normalization with symmetric diffeomorphisms for multivariate studies

Authors:
Brian Avants;Jeffrey T. Duda;H. Zhang;James C. Gee
Affiliations:
Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA;Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA;Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA;Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Year:
2007

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Abstract

Current clinical and research neuroimaging protocols acquire images using multiple modalities, for instance, T1, T2, diffusion tensor and cerebral blood flow magnetic resonance images (MRI). These multivariate datasets provide unique and often complementary anatomical and physiological information about the subject of interest. We present a method that uses fused multiple modality (scalar and tensor) datasets to perform intersubject spatial normalization. Our multivariate approach has the potential to eliminate inconsistencies that occur when normalization is performed on each modality separately. Furthermore, the multivariate approach uses a much richer anatomical and physiological image signature to infer image correspondences and perform multivariate statistical tests. In this initial study, we develop the theory for Multivariate Symmetric Normalization (MVSyN), establish its feasibility and discuss preliminary results on a multivariate statistical study of 22q deletion syndrome.