Journal article
A Robust and Reproducible Automated MRI Pipeline for Quantifying Tissue Outcomes After Experimental Stroke in Multi-Center Preclinical Networks
Imaging neuroscience (Cambridge, Mass.)
07/15/2026
DOI: 10.1162/IMAG.a.1328
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.
Details
- Title: Subtitle
- A Robust and Reproducible Automated MRI Pipeline for Quantifying Tissue Outcomes After Experimental Stroke in Multi-Center Preclinical Networks
- Creators
- Kirsten M. Lynch - University of Southern CaliforniaRyan P. Cabeen - University of Southern CaliforniaAndreia Lopes de Morais - Harvard UniversityXuyan Jin - Massachusetts General HospitalErendiz Tarakci - University of Southern CaliforniaJessica Lamb - Keck Hospital of USCBasavaraju G. Sanganahalli - Yale UniversityJelena M. Mihailovic - Yale UniversityYamileck Olivas-Garcia - University of California San DiegoDavid B. Berry - University of California San DiegoMarcio A. Diniz - Icahn School of Medicine at Mount SinaiJoseph Mandeville - Massachusetts General HospitalFahmeed Hyder - Yale UniversityDaniel R. Thedens - University of IowaAli Arbab - Augusta UniversityShuning Huang - The University of Texas Health Science CenterAdnan Bibic - Johns Hopkins UniversityWyatt Austin - Duke Medical CenterBingren Hu - University of California San DiegoMohammad B. Khan - Augusta UniversityPradip K. Kamat - Augusta UniversityArthur W. Toga - University of Southern CaliforniaPatrick Lyden - Keck Hospital of USCCenk Ayata - Harvard University
- Resource Type
- Journal article
- Publication Details
- Imaging neuroscience (Cambridge, Mass.)
- DOI
- 10.1162/IMAG.a.1328
- ISSN
- 2837-6056
- eISSN
- 2837-6056
- Publisher
- MIT Press
- Language
- English
- Electronic publication date
- 07/15/2026
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering
- Record Identifier
- 9985183561302771
Metrics
1 Record Views