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Researchers from the Mark and Mary Stevens Neuroimaging and Informatics Institute (Stevens INI) at the Keck School of Medicine of USC have developed an automated system that uses MRI to measure brain tissue changes in animal models for large, multicenter studies of experimental stroke treatments. By reducing variability among thousands of preclinical research scans from different sources, the automated system has the potential to improve the early-stage evaluation of new therapies. The system was created for the National Institutes of Health-sponsored Stroke Preclinical Assessment Network (SPAN), which tests potential treatments for acute ischemic stroke in coordinated animal studies before the most promising therapies advance toward clinical trials. The Stevens INI serves as the informatics and data core for SPAN. The open-source image analysis pipeline, described in a new study published in Imaging Neuroscience, was tested on scans from more than 2,000 mice and rats at six academic research centers. Its measurements closely matched those made by human experts and helped reduce variability associated with differences in MRI scanners and acquisition environments. Scientists have traditionally measured damage after an experimental stroke by removing the brain, staining tissue sections, and manually outlining injured areas. That process can distort tissue, depends partly on individual judgment, and captures only one point in time. MRI allows researchers to scan the same animal more than once, tracking early injury and swelling as well as tissue loss that develops later. But analyzing thousands of scans collected on different equipment creates another challenge. The scale of SPAN required a method that could process thousands of scans while applying the same rules to every image, regardless of where the data were collected. The pipeline performed about as consistently with a human reviewer as two human reviewers did with each other. The system also reduced variation linked to different scanners and imaging environments, allowing the same analysis approach to work across all six research centers.