Infarct Evolution in a Large Animal Model of Middle Cerebral Artery Occlusion

UMMS Affiliation

New England Center for Stroke Research, Department of Radiology; Image Processing and Analysis Core, Department of Radiology; Department of Neurology

Publication Date


Document Type



Cardiovascular Diseases | Nervous System Diseases | Neurology | Radiology | Translational Medical Research


Mechanical thrombectomy for the treatment of ischemic stroke shows high rates of recanalization; however, some patients still have a poor clinical outcome. A proposed reason for this relates to the fact that the ischemic infarct growth differs significantly between patients. While some patients demonstrate rapid evolution of their infarct core (fast evolvers), others have substantial potentially salvageable penumbral tissue even hours after initial vessel occlusion (slow evolvers). We show that the dog middle cerebral artery occlusion model recapitulates this key aspect of human stroke rendering it a highly desirable model to develop novel multimodal treatments to improve clinical outcomes. Moreover, this model is well suited to develop novel image analysis techniques that allow for improved lesion evolution prediction; we provide proof-of-concept that MRI perfusion-based time-to-peak maps can be utilized to predict the rate of infarct growth as validated by apparent diffusion coefficient-derived lesion maps allowing reliable classification of dogs into fast versus slow evolvers enabling more robust study design for interventional research.


Dog, Infarct growth rate, Middle cerebral artery occlusion, Perfusion MRI, Stroke, Time-to-peak

DOI of Published Version



Shazeeb MS, King RM, Brooks OW, Puri AS, Henninger N, Boltze J, Gounis MJ. Infarct Evolution in a Large Animal Model of Middle Cerebral Artery Occlusion. Transl Stroke Res. 2020 Jun;11(3):468-480. doi: 10.1007/s12975-019-00732-9. Epub 2019 Sep 3. PMID: 31478129; PMCID: PMC7051891. Link to article on publisher's site

Journal/Book/Conference Title

Translational stroke research

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Link to Article in PubMed

PubMed ID