Diffusion Tensor Uncertainty: Visualization and Similarity Metrics

Keywords

able according addition additionally adjacent among analyses analytical anisotropic anisotropy apply approaches axial become capture captures channel clustering coil complex complicated computed consisted constructed containing content context corpus corrected covariance covariances creation datasets dense describe described design develop diffusion direct directional directionality discriminant distance distances distant distributed distributions divergence divergences eigenvector elements ellipsoid employ enable error even excite expect experimental extensions fail familiar features field finally form function functions gradients gray health healthy highest hots hypothesis incorporates inherent instead interestingly interfaces isotropic least like lobular lying matrix measure measures metric metrics motion multivariate noise normally novel onto operator optimum orientation originating particularly principles proposed quality regenerative registration reliability sampling scalar scheme schemes segmentation shape shapes similarity slice solution sparser sphere spherical squared statistical still studies subject subtle suitable symmetric table takes tend tensor tensors term tissue towards trace types uncertainty underlying uniformed unit various ventricles version visualization visualize visualized whereas white