Collard, A and Bonnabel, S and Phillips, C and Sepulchre, R (2014) Anisotropy preserving DTI processing. International Journal of Computer Vision, 107. pp. 58-74. ISSN 0920-5691Full text not available from this repository.
Statistical analysis of diffusion tensor imaging (DTI) data requires a computational framework that is both numerically tractable (to account for the high dimensional nature of the data) and geometric (to account for the nonlinear nature of diffusion tensors). Building upon earlier studies exploiting a Riemannian framework to address these challenges, the present paper proposes a novel metric and an accompanying computational framework for DTI data processing. The proposed approach grounds the signal processing operations in interpolating curves. Well-chosen interpolating curves are shown to provide a computational framework that is at the same time tractable and information relevant for DTI processing. In addition, and in contrast to earlier methods, it provides an interpolation method which preserves anisotropy, a central information carried by diffusion tensor data. © 2013 Springer Science+Business Media New York.
|Uncontrolled Keywords:||Anisotropy Diffusion tensor MRI Interpolation Quaternions Riemannian manifold Spectral decomposition|
|Divisions:||Div F > Control|
|Depositing User:||Cron job|
|Date Deposited:||16 Jul 2015 13:57|
|Last Modified:||30 Nov 2015 15:53|