Departmental Papers (CIS)

Document Type

Book Chapter

Date of this Version

November 2007

Comments

Postprint version. Published in Lecture Notes in Computer Science, Volume 4791, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007, pages 359-366.
Publisher URL: http://dx.doi.org/10.1007/978-3-540-75757-3_44

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.

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Date Posted: 19 March 2008

This document has been peer reviewed.