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Large scale < /i>in vivo< //i> acquisition, segmentation, and 3D reconstruction of cortical vasculature using open-source functional ultrasound imaging platform

Publication date: 2022-01-01

Author:

Strumane, Anoek
Lambert, Théo ; Aelterman, Jan ; Babin, Danilo ; Philips, Wilfried ; Montaldo, Gabriel ; Brunner, Clément ; Urban, Alan

Keywords:

C14/18/099#54689611

Abstract:

The brain is composed of a dense and ramified vascular network comprising various sizes of arteries, veins, and capillaries. One way to assess the risk of cerebrovascular pathologies is to use computational models to predict the physiological effects of a reduction of 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. For this purpose, we have adapted our open-access functional ultrasound imaging platform previously designed for recording brain-wide activity that is now capable of fast and reproducible acquisition, segmentation, and reconstruction of the cortical vasculature. For the first time, it allows us to digitize the cortical vasculature in awake rodents with a ∼100 µm 3 spatial resolution. Contrary to most available strategies, our approach can be performed in vivo within minutes. Moreover, it is easy to implement since it neither requires exogenous contrast agents nor long post-processing time. Hence, 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, ii) quick estimation of their length. Importantly, we confirmed the relevance of our approach in a model of cortical stroke, which enables quick visualization of the ischemic lesion. This development contributes to extending the capabilities of ultrasound neuroimaging to understand better cerebrovascular pathologies such as stroke, vascular cognitive impairment, and brain tumors and is highly scalable for the clinic.