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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

5.2K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
5.2K

You might also read

Related Articles

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

Sort by
Same author

Quantitative Impact of T1 Subtraction Maps on Enhancing Component Delineation and Measured Volumes in Minimally Enhancing Pediatric Brain Tumors.

AJNR. American journal of neuroradiology·2026
Same author

High-grade glioma with dural progression: A single-center case series and review of literature.

Radiology case reports·2026
Same author

Generative AI for spatial tumor growth on MRI: a proof-of-principle study in pediatric diffuse midline glioma.

BMC medicine·2026
Same author

Personalized machine learning-guided radiation dose escalation in newly diagnosed glioblastoma: prospective pilot study.

Nature communications·2026
Same author

Multi-Institutional Annotated Multiparametric MRI Dataset of Pediatric High-Grade Gliomas.

Radiology. Artificial intelligence·2026
Same author

Congress of Neurological Surgeons Systematic Review and Evidence-Based Guidelines for the Treatment of Adults With WHO Grade II Diffuse Glioma: Update.

Neurosurgery·2026

Related Experiment Video

Updated: Jul 14, 2025

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.2K

Physics-Informed Discretization for Reproducible and Robust Radiomic Feature Extraction Using Quantitative MRI.

Walter Zhao1, Zheyuan Hu, Anahita Fathi Kazerooni

  • 1From the Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH (W.Z., Z.H., S.E.V., D.M.); Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (A.F.K., C.D.); Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (A.F.K., C.D.); Siemens Healthineers, Erlangen, Germany (G.K., M.N.); Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH (X.W.); and Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Cleveland, OH (C.B.).

Investigative Radiology
|October 9, 2023
PubMed
Summary
This summary is machine-generated.

A novel physics-informed discretization (PID) method enhances radiomic feature reproducibility from quantitative MRI. This approach offers superior robustness and scan-rescan consistency compared to conventional methods using weighted MRI.

More Related Videos

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
09:30

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

19.6K
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

624

Related Experiment Videos

Last Updated: Jul 14, 2025

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.2K
Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
09:30

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

19.6K
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

624

Area of Science:

  • Radiomics and Quantitative Magnetic Resonance Imaging (MRI).
  • Medical Imaging and Image Analysis.
  • Biomedical Engineering.

Background:

  • Radiomic features from weighted MRI show limited repeatability and reproducibility.
  • Quantitative MRI (qMRI) offers potential advantages for reproducible radiomics.
  • Novel methods are needed to improve radiomic feature extraction from qMRI.

Purpose of the Study:

  • Introduce a novel physics-informed discretization (PID) method for reproducible radiomic feature extraction.
  • Evaluate PID's performance using quantitative MRI sequences (MRF, ADC).
  • Compare PID with conventional fixed bin number (FBN) discretization.

Main Methods:

  • Prospective acquisition of multiscanner, scan-rescan quantitative (MRF T1, MRF T2, ADC) and weighted MRI data from 5 healthy subjects.
  • Extraction of first-order and texture radiomic features using PID and FBN methods.
  • Assessment of interscanner reproducibility using intraclass correlation coefficient (ICC) and analysis of variance (ANOVA).
  • Evaluation of robustness to segmentation error via simulated segmentation differences.

Main Results:

  • First-order features showed higher reproducibility in qMRI (e.g., MRF T1/T2) than weighted MRI.
  • PID significantly improved texture feature reproducibility from qMRI (MRF T1/T2) compared to FBN.
  • PID demonstrated greater robustness to segmentation errors than FBN, particularly for qMRI texture features.

Conclusions:

  • The proposed PID method yields more reproducible and robust radiomic features from qMRI than conventional FBN on weighted MRI.
  • Quantitative MRI sequences exhibit superior scan-rescan robustness and feature reproducibility compared to weighted MRI.
  • PID represents a significant advancement for reliable radiomic analysis in medical imaging.