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Finsler Geometry in Diffusion MRI. Tom Dela Haije Supervisors: Luc Florack Andrea Fuster. Connectomics. Mapping out the structure and function of the human brain. Multi-modality, Multi-scale. Palm (2010). Feusner (2007). Denk (2004). Diffusion MRI. Wedeen (2012).
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Finsler Geometry in Diffusion MRI Tom DelaHaije Supervisors: Luc Florack Andrea Fuster
Connectomics • Mapping out the structure and function of the human brain
Multi-modality, Multi-scale Palm (2010) Feusner (2007) Denk (2004)
Diffusion MRI Wedeen (2012)
Diffusion MRI - Basics • Measure diffusion locally • Correlated with fiber orientation Free diffusion Restricted diffusion
Diffusion MRI - Basics Stejskal (1965)
Diffusion Tensor Imaging • Diffusion modeled with second order positive-definite symmetric tensors Basser (1994)
Diffusion Tensor Imaging Bangera (2007)
White Matter as a Riemannian Manifold • Diffusion modeled with second order positive-definite symmetric tensors • Introducing a Riemannian metric O’Donnel (2002)
White Matter as a Riemannian Manifold • Elegant perspective: • Interpolation • Affine transformations • Tractography • Downsides: • Incompatible with complex fiber architecture
High Angular Resolution Diffusion Imaging Prčkovska(2009)
White Matter as a Finsler Manifold • Diffusion modeled with a function, homogeneous of degree 2
White Matter as a Finsler Manifold • Diffusion modeled with a function • Interpret as a Finsler manifold
Riemann-Finsler Geometry • Advantages: • Same advantages as Riemannian • Compatible with complex tissue structure • Downsides: • More difficult to measure and post-process
Project • Motivation for the metric • Validity of the DTI • Extending the Riemannian case to the Finsler case • Relating the Finsler interpretation to existing viewpoints • Operational tools for tractography and connectivity analysis