Predicting DTI Tractography Uncertainty from Diffusion-Weighted-Image Noise

Keywords

accordingly accumulates adaptive angular applicable approximated approximation around autocorrelation away axially bivariate brain branching brown circular computationally compute computed computer cone convolve cubic curve defined dependence described deviation deviations diffusion directly disappears distance distributed distribution distributions divergence easier eigenvectors equation even exhibit expect experiment fanning field fields fifth final find finds fits five fixed free future generated grid ground heavier histograms incorporate independent indicated initial integration intensive interpolation inversely island length linear local mater measure measured miles model near noise noisy nominally numerical offset offsets oriented orthogonal overlap pair parameterized plotting positions predictable predicting principal probability process projected projection properties proportional quantified radius random recipes reconstructed reconstruction repetitions root sample scale seed segment segments sets signed source space sphere square squares statistical steps streamline streamlines studies successive symmetric synthetic tails taken tangent tanner target tensor tensors thought tissue trajectories true truth twice uncertainty uniform uniformly unit updated variation vector vectors width