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Large scale in vivo acquisition, segmentation and 3D reconstruction of cortical vasculature using μDoppler ultrasound imaging

Anoek Strumane (UGent) , Théo Lambert, Jan Aelterman (UGent) , Danilo Babin (UGent) , Gabriel Montaldo, Wilfried Philips (UGent) , Clément Brunner and Alan Urban
(2025) NEUROINFORMATICS. 23(1).
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Abstract
The brain is composed of a dense and ramified vascular network of arteries, veins and capillaries of various sizes. One way to assess the risk of cerebrovascular pathologies is to use computational models to predict the physiological effects of reduced blood supply and correlate these responses with observations of brain damage. Therefore, it is crucial to establish a detailed 3D organization of the brain vasculature, which could be used to develop more accurate in silico models. To this end, we have adapted our functional ultrasound imaging platform, previously designed for recording large scale activity, to enable rapid and reproducible acquisition, segmentation and reconstruction of the cortical vasculature. For the first time, it allows us to digitize the cortical similar to 100 - mu m3 spatial resolution. Unlike most available strategies, our approach can be performed in vivo within minutes. Moreover, it is easy to implement since it requires neither exogenous contrast agents nor long post-processing time. Therefore, we performed a cortex-wide reconstruction of the vasculature and its quantitative analysis, including i) classification of descending arteries versus ascending veins in more than 1500 vessels/animal and ii) rapid estimation of their length. Importantly, we confirmed the relevance of our approach in a model of cortical stroke, which allows rapid visualization of the ischemic lesion. This development contributes to extending the capabilities of ultrasound neuroimaging to better understand cerebrovascular pathologies such as stroke, vascular cognitive impairment and brain tumors, and is highly scalable for the clinic.
Keywords
Brain vasculature, Blood vessel segmentation, 3D models, Stroke, Whole-brain ultrasound imaging, BRAIN, ANGIOGENESIS, TOMOGRAPHY, TRACKING

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MLA
Strumane, Anoek, et al. “Large Scale in Vivo Acquisition, Segmentation and 3D Reconstruction of Cortical Vasculature Using ΜDoppler Ultrasound Imaging.” NEUROINFORMATICS, vol. 23, no. 1, 2025, doi:10.1007/s12021-024-09706-1.
APA
Strumane, A., Lambert, T., Aelterman, J., Babin, D., Montaldo, G., Philips, W., … Urban, A. (2025). Large scale in vivo acquisition, segmentation and 3D reconstruction of cortical vasculature using μDoppler ultrasound imaging. NEUROINFORMATICS, 23(1). https://doi.org/10.1007/s12021-024-09706-1
Chicago author-date
Strumane, Anoek, Théo Lambert, Jan Aelterman, Danilo Babin, Gabriel Montaldo, Wilfried Philips, Clément Brunner, and Alan Urban. 2025. “Large Scale in Vivo Acquisition, Segmentation and 3D Reconstruction of Cortical Vasculature Using ΜDoppler Ultrasound Imaging.” NEUROINFORMATICS 23 (1). https://doi.org/10.1007/s12021-024-09706-1.
Chicago author-date (all authors)
Strumane, Anoek, Théo Lambert, Jan Aelterman, Danilo Babin, Gabriel Montaldo, Wilfried Philips, Clément Brunner, and Alan Urban. 2025. “Large Scale in Vivo Acquisition, Segmentation and 3D Reconstruction of Cortical Vasculature Using ΜDoppler Ultrasound Imaging.” NEUROINFORMATICS 23 (1). doi:10.1007/s12021-024-09706-1.
Vancouver
1.
Strumane A, Lambert T, Aelterman J, Babin D, Montaldo G, Philips W, et al. Large scale in vivo acquisition, segmentation and 3D reconstruction of cortical vasculature using μDoppler ultrasound imaging. NEUROINFORMATICS. 2025;23(1).
IEEE
[1]
A. Strumane et al., “Large scale in vivo acquisition, segmentation and 3D reconstruction of cortical vasculature using μDoppler ultrasound imaging,” NEUROINFORMATICS, vol. 23, no. 1, 2025.
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  abstract     = {{The brain is composed of a dense and ramified vascular network of arteries, veins and capillaries of various sizes. One way to assess the risk of cerebrovascular pathologies is to use computational models to predict the physiological effects of reduced blood supply and correlate these responses with observations of brain damage. Therefore, it is crucial to establish a detailed 3D organization of the brain vasculature, which could be used to develop more accurate in silico models. To this end, we have adapted our functional ultrasound imaging platform, previously designed for recording large scale activity, to enable rapid and reproducible acquisition, segmentation and reconstruction of the cortical vasculature. For the first time, it allows us to digitize the cortical similar to 100 - mu m3 spatial resolution. Unlike most available strategies, our approach can be performed in vivo within minutes. Moreover, it is easy to implement since it requires neither exogenous contrast agents nor long post-processing time. Therefore, we performed a cortex-wide reconstruction of the vasculature and its quantitative analysis, including i) classification of descending arteries versus ascending veins in more than 1500 vessels/animal and ii) rapid estimation of their length. Importantly, we confirmed the relevance of our approach in a model of cortical stroke, which allows rapid visualization of the ischemic lesion. This development contributes to extending the capabilities of ultrasound neuroimaging to better understand cerebrovascular pathologies such as stroke, vascular cognitive impairment and brain tumors, and is highly scalable for the clinic.}},
  articleno    = {{5}},
  author       = {{Strumane, Anoek and Lambert, Théo and Aelterman, Jan and Babin, Danilo and Montaldo, Gabriel and Philips, Wilfried and Brunner, Clément and Urban, Alan}},
  issn         = {{1539-2791}},
  journal      = {{NEUROINFORMATICS}},
  keywords     = {{Brain vasculature,Blood vessel segmentation,3D models,Stroke,Whole-brain ultrasound imaging,BRAIN,ANGIOGENESIS,TOMOGRAPHY,TRACKING}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{12}},
  title        = {{Large scale in vivo acquisition, segmentation and 3D reconstruction of cortical vasculature using μDoppler ultrasound imaging}},
  url          = {{http://doi.org/10.1007/s12021-024-09706-1}},
  volume       = {{23}},
  year         = {{2025}},
}
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