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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Comparative Seizure Outcomes of Vagus Nerve Stimulation, Deep Brain Stimulation, and Their Combination in Lennox-Gastaut Syndrome.

Annals of neurology·2026
Same author

Kainic acid pig model of hippocampal epilepsy.

Scientific reports·2026
Same author

Comparison of clinical outcomes in adamantinomatous and papillary craniopharyngioma: a baseline analysis before the targeted therapy era.

Neurosurgical focus·2026
Same author

Predictors of hospital length of stay, discharge disposition, and readmission after craniopharyngioma surgery: a multicenter study from the RAPID database.

Neurosurgical focus·2026
Same author

Benchmarking Surgical Outcomes for Endoscopic Transnasal Craniopharyngioma Resection: Implications for Clinical Practice From the RAPID Consortium.

Operative neurosurgery (Hagerstown, Md.)·2026
Same author

Standardized Perioperative Protocols Are Associated With Reduced Length of Stay and Readmission in Cushing Disease: Results From the Multicenter RAPID Study.

Neurosurgery·2026

Related Experiment Video

Updated: May 24, 2025

In Vivo Morphometric Analysis of Human Cranial Nerves Using Magnetic Resonance Imaging in Menière's Disease Ears and Normal Hearing Ears
10:27

In Vivo Morphometric Analysis of Human Cranial Nerves Using Magnetic Resonance Imaging in Menière's Disease Ears and Normal Hearing Ears

Published on: February 21, 2018

10.5K

Stiffness analysis of meningiomas using neural network-based inversion on MR Elastography.

Keni Zheng, Matthew Murphy, Emanuele Camerucci

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    Summary

    This study introduces a novel machine learning method to accurately measure meningioma tumor stiffness using MR Elastography. This technique correlates pre-operative tumor consistency with surgical outcomes, aiding in better treatment planning for brain tumors.

    More Related Videos

    Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
    06:09

    Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

    Published on: July 21, 2023

    1.1K
    Viscoelastic Characterization of Soft Tissue-Mimicking Gelatin Phantoms using Indentation and Magnetic Resonance Elastography
    07:57

    Viscoelastic Characterization of Soft Tissue-Mimicking Gelatin Phantoms using Indentation and Magnetic Resonance Elastography

    Published on: May 10, 2022

    2.0K

    Related Experiment Videos

    Last Updated: May 24, 2025

    In Vivo Morphometric Analysis of Human Cranial Nerves Using Magnetic Resonance Imaging in Menière's Disease Ears and Normal Hearing Ears
    10:27

    In Vivo Morphometric Analysis of Human Cranial Nerves Using Magnetic Resonance Imaging in Menière's Disease Ears and Normal Hearing Ears

    Published on: February 21, 2018

    10.5K
    Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
    06:09

    Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

    Published on: July 21, 2023

    1.1K
    Viscoelastic Characterization of Soft Tissue-Mimicking Gelatin Phantoms using Indentation and Magnetic Resonance Elastography
    07:57

    Viscoelastic Characterization of Soft Tissue-Mimicking Gelatin Phantoms using Indentation and Magnetic Resonance Elastography

    Published on: May 10, 2022

    2.0K

    Area of Science:

    • Neurosurgery
    • Medical Imaging
    • Biophysics

    Background:

    • Meningiomas are common benign brain tumors requiring surgical removal.
    • Tumor stiffness is a critical factor influencing surgical approaches.
    • Accurate assessment of mechanical properties is essential for surgical planning.

    Purpose of the Study:

    • To develop and validate a machine learning-based MR Elastography (MRE) inversion method for estimating meningioma mechanical properties.
    • To investigate the relationship between pre-operative MRE-derived tumor consistency and post-operative extent of resection (EOR).

    Main Methods:

    • An artificial neural network was trained using synthetic displacement field data for MRE inversion.
    • The method reduces partial volume effects and accounts for tumor heterogeneity, improving stiffness estimation (R²=0.93).
    • A cohort of 52 patients with meningiomas was analyzed, focusing on skull-based tumors.

    Main Results:

    • The developed MRE method accurately estimates meningioma stiffness, outperforming traditional methods by reducing simulation assumptions.
    • A significant correlation (p=0.024) was found between pre-operative MRE-based tumor consistency and post-operative EOR in skull-based meningiomas.
    • The study highlights the potential of MRE in predicting surgical outcomes.

    Conclusions:

    • Machine learning-enhanced MRE provides a robust framework for quantifying meningioma mechanical properties.
    • Pre-operative assessment of tumor consistency using MRE may offer valuable insights into surgical resectability and outcomes.
    • This approach can aid neurosurgeons in optimizing surgical strategies for meningioma treatment.