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Abstract #3557

Diffusion Tensor Uncertainty: Visualization and Similarity Metrics

Mustafa Okan Irfanoglu1, 2, Michael Curry1, Evren zarslan1, 2, Cheng Guan Koay1, Sinisa Pajevic1, Peter J. Basser1

1NIH, NICHD, Bethesda, MD, United States; 2Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States

The uncertainty inherent in the estimated diffusion tensors can have profound effects on the analysis outcomes. In this word, we propose a novel method to visualize DTI uncertainty along with similarity metrics that incorporate this information. These metrics can be employed in a variety of applications including tensor field image registration or tensor image segmentation.