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Frequency-dependent diffusion tensor distribution imaging in the evaluation of ischemic stroke

Gröhn, Sara ; Naranjo, Ángela ; Narvaez, Omar ; Yon, Maxime LU ; Buz-Yalug, Buse ; Blanco, Santos ; Topgaard, Daniel LU ; Martinez-Lara, Esther ; Ángeles Peinado, Ma and Tohka, Jussi , et al. (2026) In Neuroscience Informatics 6(2).
Abstract

Non-invasive MRI is widely used to assess and monitor ischemic stroke, yet conventional approaches often lack sensitivity to subtle microstructural changes and struggle to evaluate tissue viability across lesion, penumbra, and distal regions. In this study, frequency dependent diffusion tensor distribution imaging (ωDTD) was combined with clustering of diffusion tensor distributions D(ω) and multivariate regression modeling to characterize ischemic tissue alterations in a whole brain section. Ex vivo ωDTD and histology were performed in rats subjected to middle cerebral artery occlusion (MCAO) or sham surgery (P = 11) 24 h after reperfusion. Lesions showed cell loss and an increased presence of smaller, likely glial, cells. A random... (More)

Non-invasive MRI is widely used to assess and monitor ischemic stroke, yet conventional approaches often lack sensitivity to subtle microstructural changes and struggle to evaluate tissue viability across lesion, penumbra, and distal regions. In this study, frequency dependent diffusion tensor distribution imaging (ωDTD) was combined with clustering of diffusion tensor distributions D(ω) and multivariate regression modeling to characterize ischemic tissue alterations in a whole brain section. Ex vivo ωDTD and histology were performed in rats subjected to middle cerebral artery occlusion (MCAO) or sham surgery (P = 11) 24 h after reperfusion. Lesions showed cell loss and an increased presence of smaller, likely glial, cells. A random forest (RF) model was used to explain and predict histological parameters from diffusion tensor imaging (DTI), manually bin resolved ωDTD features, and cluster resolved ωDTD parameters. Model performance was evaluated using leave one animal out cross validation (LOO CV). ωDTD features better represented cell number than DTI metrics (ωDTD R2 = 0.73 vs. DTI R2 = 0.49), with similar advantages for nuclear area and circularity (ωDTD R2 = 0.64 and 0.61 vs. DTI R2 = 0.40 and 0.35). The RF model further proved beneficial in capturing complex, nonlinear relationships between MRI parameters and tissue characteristics. Overall, these results indicate that ωDTD provides richer microstructural information than standard DTI, and that combining ωDTD with advanced machine learning methods enhances interpretation of ischemic tissue damage.

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type
Contribution to journal
publication status
published
subject
keywords
Clustering, Diffusion tensor distribution imaging, Frequency-dependent diffusion tensor distribution imaging, Histology, Ischemic stroke, MCAO, MRI, Non-linear regression, Non-parametric distributions, Random forest (RF)
in
Neuroscience Informatics
volume
6
issue
2
article number
100270
publisher
Elsevier
external identifiers
  • scopus:105034784113
ISSN
2772-5286
DOI
10.1016/j.neuri.2026.100270
language
English
LU publication?
yes
id
11b7dd64-f53e-4e9e-9b88-42f9292b07d5
date added to LUP
2026-05-13 11:51:04
date last changed
2026-05-29 13:21:09
@article{11b7dd64-f53e-4e9e-9b88-42f9292b07d5,
  abstract     = {{<p>Non-invasive MRI is widely used to assess and monitor ischemic stroke, yet conventional approaches often lack sensitivity to subtle microstructural changes and struggle to evaluate tissue viability across lesion, penumbra, and distal regions. In this study, frequency dependent diffusion tensor distribution imaging (ωDTD) was combined with clustering of diffusion tensor distributions D(ω) and multivariate regression modeling to characterize ischemic tissue alterations in a whole brain section. Ex vivo ωDTD and histology were performed in rats subjected to middle cerebral artery occlusion (MCAO) or sham surgery (P = 11) 24 h after reperfusion. Lesions showed cell loss and an increased presence of smaller, likely glial, cells. A random forest (RF) model was used to explain and predict histological parameters from diffusion tensor imaging (DTI), manually bin resolved ωDTD features, and cluster resolved ωDTD parameters. Model performance was evaluated using leave one animal out cross validation (LOO CV). ωDTD features better represented cell number than DTI metrics (ωDTD R2 = 0.73 vs. DTI R2 = 0.49), with similar advantages for nuclear area and circularity (ωDTD R2 = 0.64 and 0.61 vs. DTI R2 = 0.40 and 0.35). The RF model further proved beneficial in capturing complex, nonlinear relationships between MRI parameters and tissue characteristics. Overall, these results indicate that ωDTD provides richer microstructural information than standard DTI, and that combining ωDTD with advanced machine learning methods enhances interpretation of ischemic tissue damage.</p>}},
  author       = {{Gröhn, Sara and Naranjo, Ángela and Narvaez, Omar and Yon, Maxime and Buz-Yalug, Buse and Blanco, Santos and Topgaard, Daniel and Martinez-Lara, Esther and Ángeles Peinado, Ma and Tohka, Jussi and Sierra, Alejandra}},
  issn         = {{2772-5286}},
  keywords     = {{Clustering; Diffusion tensor distribution imaging; Frequency-dependent diffusion tensor distribution imaging; Histology; Ischemic stroke; MCAO; MRI; Non-linear regression; Non-parametric distributions; Random forest (RF)}},
  language     = {{eng}},
  number       = {{2}},
  publisher    = {{Elsevier}},
  series       = {{Neuroscience Informatics}},
  title        = {{Frequency-dependent diffusion tensor distribution imaging in the evaluation of ischemic stroke}},
  url          = {{http://dx.doi.org/10.1016/j.neuri.2026.100270}},
  doi          = {{10.1016/j.neuri.2026.100270}},
  volume       = {{6}},
  year         = {{2026}},
}