Divergence from Diffusion Tensor: a discriminant diffusion marker from single shell MRI
- 1Universidad de Valladolid
Abstract
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.
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BibTeX
@misc{fernandez2026divergence,
title = {{Divergence from Diffusion Tensor: a discriminant diffusion marker from single shell MRI}},
author = {S. Aja Fernandez and A. Tristan-Vega},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.26.26366463},
url = {https://www.medrxiv.org/content/10.64898/2026.10.26.26366463v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Divergence from Diffusion Tensor: a discriminant diffusion marker from single shell MRI
AU - S. Aja Fernandez
AU - A. Tristan-Vega
PY - 2026
DA - 2026/10/26
PB - medRxiv
DO - 10.64898/2026.10.26.26366463
UR - https://www.medrxiv.org/content/10.64898/2026.10.26.26366463v1
AB - 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.
ER -