Translation of acute ischemic stroke research to the clinical setting remains limited over the last few decades with only one drug, recombinant tissue-type plasminogen activator, successfully completing the path from experimental study to clinical practice. To improve the selection of experimental treatments before testing in clinical studies, the use of large gyrencephalic animal models of acute ischemic stroke has been recommended. Currently, these models include, among others, dogs, swine, sheep, and nonhuman primates that closely emulate aspects of the human setting of brain ischemia and reperfusion. Species-specific characteristics, such as the cerebrovascular architecture or pathophysiology of thrombotic/ischemic processes, significantly influence the suitability of a model to address specific research questions. In this article, we review key characteristics of the main large animal models used in translational studies of acute ischemic stroke, regarding (1) anatomy and physiology of the cerebral vasculature, including brain morphology, coagulation characteristics, and immune function; (2) ischemic stroke modeling, including vessel occlusion approaches, reproducibility of infarct size, procedural complications, and functional outcome assessment; and (3) implementation aspects, including ethics, logistics, and costs. This review specifically aims to facilitate the selection of the appropriate large animal model for studies on acute ischemic stroke, based on specific research questions and large animal model characteristics.

Background: X-ray digital subtraction angiography (DSA) is the imaging modality for peri-procedural guidance and treatment evaluation in (neuro-) vascular interventions. Perfusion image construction from DSA, as a means of quantitatively depicting cerebral hemodynamics, has been shown feasible. However, the quantitative property of perfusion DSA has not been well studied. Purpose: To comparatively study the independence of deconvolution-based perfusion DSA with respect to varying injection protocols,as well as its sensitivity to alterations in brain conditions. Methods: We developed a deconvolution-based algorithm to compute perfusion parametric images from DSA, including cerebral blood volume (CBV DSA ), cerebral blood flow (CBF DSA ), time to maximum (Tmax), and mean transit time (MTT DSA ) and applied it to DSA sequences obtained from two swine models. We also extracted the time intensity curve (TIC)-derived parameters,that is,area under the curve (AUC), peak concentration of the curve, and the time to peak (TTP) from these sequences. Deconvolution-based parameters were quantitatively compared to TIC-derived parameters in terms of consistency upon variations in injection profile and time resolution of DSA, as well as sensitivity to alterations of cerebral condition. Results: Comparing to TIC-derived parameters, the standard deviation (SD) of deconvolution-based parameters (normalized with respect to the mean) are two to five times smaller, indicating that they are more consistent across different injection protocols and time resolutions. Upon ischemic stroke induced in a swine model, the sensitivities of deconvolution-based parameters are equal to, if not higher than, those of TIC-derived parameters. Conclusions: In comparison to TIC-derived parameters, deconvolution-based perfusion imaging in DSA shows significantly higher quantitative reliability against variations in injection protocols across different time resolutions, and is sensitive to alterations in cerebral hemodynamics. The quantitative nature of perfusion angiography may allow for objective treatment assessment in neurovascular interventions.