A robust and reproducible automated MRI pipeline for quantifying tissue outcomes after experimental stroke in multi-center preclinical networks.
Authors
Affiliations (18)
Affiliations (18)
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States.
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States.
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute of the Keck School of Medicine of USC, Los Angeles, CA, United States.
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States.
- Department of Emergency Medicine, University of California, San Diego, La Jolla, CA, United States.
- Department of Orthopaedic Surgery, University of California, San Diego, La Jolla, CA, United States.
- Icahn School of Medicine at Mount Sinai, New York, NY, United States.
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States.
- Department of Radiology, University of Iowa, Iowa City, IA, United States.
- Department of Biochemistry and Molecular Biology, Medical College of Georgia at Augusta University, Augusta, GA, United States.
- Georgia Cancer Center, Augusta University, Augusta, CA, United States.
- Department of Diagnostic and Interventional Imaging, The University of Texas McGovern Medical School at Houston, Houston, TX, United States.
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, United States.
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
- Department of Radiology, Duke University Medical Center, Durham, NC, United States.
- Department of Neurology, Medical College of Georgia at Augusta University, Augusta, GA, United States.
- Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA, United States.
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States.
Abstract
The failure to translate promising preclinical stroke therapies into clinical success is a multi-faceted problem; however, a critical contributing factor is the lack of rigorous, reproducible preclinical outcome measures. While magnetic resonance imaging (MRI) offers a translational alternative to traditional histology, its use in large, multi-site trials is challenged by data heterogeneity and the need for scalable analysis. To address this, we developed and validated a fully automated, open-source image analysis pipeline for the Stroke Preclinical Assessment Network (SPAN), a six-center preclinical trial network. The pipeline processed T2-weighted and apparent diffusion coefficient (ADC) maps from over 2,000 mice and rats, incorporating steps for cross-site data harmonization, deep learning-based brain extraction, and rule-based segmentation to quantify infarct volume, brain swelling, and atrophy. The pipeline demonstrated high accuracy, as automated lesion volumes strongly correlated with manual expert tracing on both MRI (R = 0.96) and 2,3,5-triphenyl-tetrazolium chloride (TTC)-stained tissue (R = 0.86). The U-net model for brain extraction achieved a Dice score of 0.96, and our harmonization method successfully reduced inter-site variability in quantitative MRI parameters. This robust and reproducible pipeline provides a scalable framework for standardizing tissue outcome assessment, enhancing the rigor of multi-site preclinical studies.