Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

MRI based diffusion and perfusion predictive model to estimate stroke evolution.

S E Rose1, J B Chalk, M P Griffin

  • 1Centre For Magnetic Resonance, University of Queensland, Brisbane, Queensland 4072, Australia. Stephen.Rose@cmr.uq.edu.au

Magnetic Resonance Imaging
|November 17, 2001
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Cabozantinib plus nivolumab and ipilimumab in previously untreated, advanced renal cell carcinoma: final results and biomarker analyses from the phase III COSMIC-313 study.

Annals of oncology : official journal of the European Society for Medical Oncology·2026
Same author

Differences in white matter detected by ex vivo 9.4 T MRI are associated with axonal changes in the R6/1 model of Huntington's disease.

Neurobiology of disease·2026
Same author

UNITY: A low-field magnetic resonance neuroimaging initiative to characterize neurodevelopment in low and middle-income settings.

Developmental cognitive neuroscience·2024
Same author

Authors' Response to Correspondence Re Ethnic Differences in Radiotherapy Outcomes in a Majority South Asian Leicester Community.

Clinical oncology (Royal College of Radiologists (Great Britain))·2024
Same author

Oral Cavity Cancers: Ethnic Differences in Radiotherapy Outcomes in a Majority South Asian Leicester Community.

Clinical oncology (Royal College of Radiologists (Great Britain))·2024
Same author

Genetic risk for schizophrenia is associated with increased proportion of indirect connections in brain networks revealed by a semi-metric analysis: evidence from population sample stratified for polygenic risk.

Cerebral cortex (New York, N.Y. : 1991)·2022
Same journal

Reconstruction of MRI from undersampled k-spaces of double-contrast volume acquisitions using deep neural networks.

Magnetic resonance imaging·2026
Same journal

Radiofrequency-induced heating safety of brain MRI scans at 7 T in the presence of a shoulder implant.

Magnetic resonance imaging·2026
Same journal

Incremental diagnostic value of microstructural time-dependent diffusion MRI in differentiating PCNSL from glioblastoma over conventional MRI.

Magnetic resonance imaging·2026
Same journal

Enhanced motion compensation for free-breathing dynamic contrast-enhanced MRI with GROG-facilitated bunch phase encoding and Golden angle radial sampling.

Magnetic resonance imaging·2026
Same journal

The allegory of the cave: 10 years of AI shadows in radiology.

Magnetic resonance imaging·2026
Same journal

Conversion of 3 T liver, spleen, pancreas, and kidney R2* measurements to 1.5 T R2* equivalents: Validation of a theoretical framework.

Magnetic resonance imaging·2026
See all related articles

This study introduces an automated method using MR imaging to predict stroke infarct evolution. The strategy accurately identifies tissue likely to infarct or recover, aiding in potential therapeutic interventions.

Area of Science:

  • Neurology
  • Medical Imaging
  • Computational Biology

Background:

  • Stroke is a leading cause of disability, necessitating accurate prediction of infarct evolution for timely treatment.
  • Current methods for predicting infarct progression can be time-consuming and may lack precision.
  • Magnetic Resonance (MR) imaging offers detailed insights into brain tissue status during acute stroke.

Purpose of the Study:

  • To develop and validate a novel automated strategy for predicting infarct evolution in acute stroke patients.
  • To assess the utility of MR diffusion and perfusion imaging in quantifying tissue viability and hemodynamic function.
  • To establish a predictive model for infarct growth that can guide therapeutic decisions and clinical trial evaluations.

Main Methods:

  • An automated strategy was developed using MR diffusion and perfusion (DWI/PI) images from acute stroke patients.

Related Experiment Videos

  • Regions-of-interest (ROIs) for initial diffusion lesions and abnormal hemodynamic function (mean transit time - MTT) were automatically extracted.
  • Quantitative measures including cerebral blood flow (CBF), cerebral blood volume (CBV), and their ratio measures (r(a)CBF, r(a)CBV) were calculated.
  • A parametric normal classifier algorithm was employed to predict infarct growth based on these quantitative measures.

    • Main Results:

    • Significant differences in r(a)CBF and r(a)CBV were observed between eventually infarcted and recovered MTT tissue (p < 0.003 and p < 0.001).
    • Mean absolute CBF and CBV also differed significantly between infarcted and recovered tissues (p < 0.009 and p < 0.036).
    • The automated strategy demonstrated high performance on validation data, with sensitivity of 0.72 and specificity of 0.97 for modeling infarct evolution.

    Conclusions:

    • The automated MR imaging strategy effectively predicts infarct evolution in acute stroke.
    • This methodology provides quantitative insights into tissue hemodynamic status, differentiating between salvageable and non-salvageable brain tissue.
    • The proposed approach holds potential for guiding therapeutic interventions in stroke patients and evaluating novel stroke therapies in clinical trials.