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On What Manifold do Diffusion Tensors Live?

Basser, Peter J., Pasternak, Ofer, Sochen, Nir, Verma, Ragini
School of Computer Science, Tel Aviv University, Israel, 69978.
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/1501
Published in The MIDAS Journal - MICCAI 2008 Workshop: Manifolds in Medical Imaging: Metrics, Learning and Beyond.
Submitted by Ofer Pasternak on 2008-09-12 11:32:52.

Diffusion tensor imaging has become an important research and clinical tool, owing to its unique ability to infer microstructural properties of living tissue. Increased use has led to a demand for statistical tools to analyze diffusion tensor data and perform, for example, confidence estimates, ROI analysis, and group comparisons. A first step towards developing a statistical framework is establishing the basic notion of distance between tensors. We investigate the properties of two previously proposed metrics that define a Riemannian manifold: the affine-invariant and Euclidean metrics. We find that the Euclidean metric is more appropriate for intra-voxel comparisons, and suggest that a context-dependent metric may be required for inter-voxel comparisons.