Divergence from Diffusion Tensor: a discriminant diffusion marker from single shell MRI

Purpose: The Divergence from the Diffusion Tensor (DDT) is a new diffusion MRI metric that quantifies how the measured diffusion signal separates from the tensor assumption. It is marker for tissue heterogeneity that is estimated from single-shell data and can be used in brain studies. Methods: There exist other techniques that allow the calculation of how the measured diffusion diverges from the Gaussian and tensor assumptions. However, the calculation requires large data-sets with different b-values, which makes them incompatible which standard diffusion tensor acquisitions. By imposing an initial assumption, an alternative formulation based on one single shell (ie, single b-value) is reported. Results: Publicly available databases are used to test the behavior of DDT against other anisotropy measures. It shows the ability to discriminate different non-Gaussian diffusion scenarios with very low intra-subject variabilities. Conclusions: DDT can extract relevant information from the white matter, without any additional demands on data acquisition and totally compatible with standard diffusion MRI analysis pipelines. This makes DDT a good candidate to be used as an imaging marker for brain studies.