Multivariate normalization with symmetric diffeomorphisms for multivariate studies.
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.
|Title:||Multivariate normalization with symmetric diffeomorphisms for multivariate studies.|
|Keywords:||Adult, Algorithms, Artificial Intelligence, Brain, Demyelinating Diseases, DiGeorge Syndrome, Diffusion Magnetic Resonance Imaging, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Multivariate Analysis, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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