Divergence from Diffusion Tensor: a discriminant diffusion marker from single shell MRI
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This article is not in any list yet, why not save it to one of your lists.Abstract
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.
