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.), Vol.4, IMAGa1328
08/07/2026
DOI: 10.1162/IMAG.a.1328
PMCID: PMC13455521
PMID: 42577804
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.), Vol.4, IMAGa1328
- DOI
- 10.1162/IMAG.a.1328
- PMID
- 42577804
- PMCID
- PMC13455521
- ISSN
- 2837-6056
- eISSN
- 2837-6056
- Publisher
- MIT Press
- Grant note
- NIH: R01 NS099455, 1UO1NS113356, R01NS112511, R01NS110378, R01NS117565, 19TPA34850076, U01NS113444, R01NS102583, R01NS105894, U01NS113443, U01NS113445, R35HL139926, R01NS109910, U01NS113388, U24NS107247, U01NS113451, S10-OD032343-01, U24NS113452, P41EB015922 NIH National Center for Advancing Translational Science (NCATS) UCLA CTSI grant: UL1 TR001881-01 Office of the Director, National Institutes of Health: S10OD032285 Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation: 2020-225670
This work was supported by NIH grants R01 NS099455, 1UO1NS113356, and R01NS112511 (to D.C.H.); R01NS110378 and R01NS117565 (to K.D.); 19TPA34850076 (to A.S.A.); U01NS113444, R01NS102583, and R01NS105894 (to R.C.K.); U01NS113443 (to C.A.); U01NS113445 (to L.H.S.); R35HL139926 and R01NS109910 (to A.K.C.); U01NS113388 (to A.K.C. and E.C.L.); U24NS107247 (to E.C.L.); U01NS113451 (to L.D.M. and J.A.); and S10-OD032343-01 (to D.B.B. and Y.O.-G.); U24NS113452 (to P.D.L.) and NIH National Center for Advancing Translational Science (NCATS) UCLA CTSI grant UL1 TR001881-01 (to A.R. and M.A.D.). The Laboratory of Neuro Imaging Resource (LONIR) at USC is supported in part by NIH (grant number P41EB015922 to A. Toga). Research reported in this publication was supported by Office of the Director, National Institutes of Health under Award Number S10OD032285 (to A. Toga). R.P.C. is supported in part by grant number 2020-225670 from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation.
- Language
- English
- Electronic publication date
- 07/15/2026
- Date published
- 08/07/2026
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering
- Record Identifier
- 9985183561302771
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