A new signal model is introduced for diffusion tensor distribution imaging which is monotonically decreasing for all b-values unlike the cumulant and kurtosis models. A constrained multi-normal distribution is used as the tensor distribution which is fully characterized by the 2nd order mean and 4th order covariance tensors. A theoretical framework is presented showing the richness of covariance tensor, using synthetic gray and white matter voxels, and the ability to estimate the mean and covariance tensor from noisy MR signal.
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