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

Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

135
Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
135
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

50
Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
50
Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

Radiological Investigation II: MRI and Ventilation Perfusion Scan

219
Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
219

You might also read

Related Articles

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

Sort by
Same author

Spectrum of Breast Sarcomas with Radiologic-Pathologic Correlation.

Radiographics : a review publication of the Radiological Society of North America, Inc·2026
Same author

Hypertension is related to a slower radiotracer removal from lateral ventricles.

bioRxiv : the preprint server for biology·2026
Same author

Cardiac-Gated Diffusion-Weighted Magnetic Resonance Imaging Assessment of Kidney Function in Patients With Kidney Cancer.

Kidney international reports·2026
Same author

Assessing the Effectiveness of the <i>Radiology</i> In Training Program in Fostering Highly Skilled Reviewers.

Radiology·2026
Same author

Abbreviated MRI for the Evaluation of Treatment Response in Patients Undergoing Neoadjuvant Chemotherapy for Breast Cancer.

Radiology·2026
Same author

Pembrolizumab in Combination With Gemcitabine and Concurrent Hypofractionated Radiation Therapy as Bladder-sparing Treatment for Muscle-invasive Urothelial Cancer of the Bladder: A Multicenter Phase 2 Trial.

European urology·2026
Same journal

Reading the River: What the Venous System Tells Us About IVC Wall Invasion in Renal Cell Carcinoma.

Radiology. Imaging cancer·2026
Same journal

TROP2-targeted NIR-II Peptide Probe for Intraoperative Detection of Tumor Margins and Sentinel Nodes in Breast-conserving Surgery.

Radiology. Imaging cancer·2026
Same journal

AI-assisted Radiomic Model for Cervical Cancer Recurrence Prediction: A Multicenter Retrospective Study with Experimental Validation.

Radiology. Imaging cancer·2026
Same journal

Synthetic MRI and Delta Histogram Analysis: A Step Forward in Noninvasive Meningioma Grading.

Radiology. Imaging cancer·2026
Same journal

Preoperative MRI Model for Predicting Inferior Vena Cava Wall Invasion in Renal Cell Carcinoma with Tumor Thrombus.

Radiology. Imaging cancer·2026
Same journal

Liver Background Uptake of Prostate-specific Membrane Antigen-targeted PET Radiotracers: A Systematic Review and Meta-Analysis.

Radiology. Imaging cancer·2026
See all related articles

Related Experiment Video

Updated: Sep 9, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.6K

Evaluating Breast Cancer Intravoxel Incoherent Motion MRI Biomarkers across Software Platforms.

Eric E Sigmund1, Gene Y Cho1,2,3, Dibash Basukala1,4

  • 1Center for Biomedical Imaging, Center for Advanced Innovation and Imaging Research (CAIIR), Department of Radiology, NYU Langone Health, 660 1st Ave, New York, NY 10016.

Radiology. Imaging Cancer
|September 5, 2025
PubMed
Summary
This summary is machine-generated.

Intravoxel incoherent motion (IVIM) biomarkers show translational potential for breast cancer characterization, with specific metrics demonstrating high diagnostic accuracy across different MRI vendors and software. This technology offers a promising tool for improving breast cancer diagnosis.

Keywords:
BreastMR-Diffusion Weighted ImagingTechnology Assessment

More Related Videos

Author Spotlight: Integrating High-Resolution Intravital Imaging and MRI to Enhance Stereotactic Body Radiation Therapy Planning
10:25

Author Spotlight: Integrating High-Resolution Intravital Imaging and MRI to Enhance Stereotactic Body Radiation Therapy Planning

Published on: April 12, 2024

1.6K
Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model
08:32

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Published on: October 2, 2020

6.4K

Related Experiment Videos

Last Updated: Sep 9, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.6K
Author Spotlight: Integrating High-Resolution Intravital Imaging and MRI to Enhance Stereotactic Body Radiation Therapy Planning
10:25

Author Spotlight: Integrating High-Resolution Intravital Imaging and MRI to Enhance Stereotactic Body Radiation Therapy Planning

Published on: April 12, 2024

1.6K
Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model
08:32

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Published on: October 2, 2020

6.4K

Area of Science:

  • Radiology
  • Biomedical Imaging
  • Oncology

Background:

  • Intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) offers advanced insights into tissue microenvironment.
  • Standardization of IVIM biomarkers across different MRI vendors and software is crucial for clinical translation.
  • Breast cancer characterization relies on accurate and reproducible imaging biomarkers.

Purpose of the Study:

  • To evaluate the consistency and diagnostic performance of IVIM biomarkers for breast cancer characterization.
  • To compare IVIM metrics derived from different MRI vendors and analysis software.
  • To assess the translational potential of IVIM for differentiating benign from malignant breast lesions.

Main Methods:

  • Retrospective analysis of diffusion-weighted imaging (DWI) data from 106 patients across two sites.
  • Acquisition of DWI using 1.5-T and 3-T MRI scanners from two vendors with multiple b-values.
  • Derivation and comparison of IVIM parameters (Dt, fp, Dp) and their radiomics using two software packages (Igor and Firevoxel).
  • Statistical analysis including Bland-Altman, logistic regression, and ROC curve analysis to assess diagnostic performance.

Main Results:

  • Tissue diffusivity (Dt) showed the highest software consistency across sites.
  • Individual IVIM metrics like Dt,min, fp,max, and Dp,max demonstrated significant diagnostic value (AUCs ranging from 0.75 to 0.835).
  • Multivariable models incorporating five key IVIM metrics achieved high diagnostic accuracy (AUCs up to 0.90 ± 0.03).

Conclusions:

  • IVIM biomarkers are reproducible and hold significant translational potential for breast cancer characterization.
  • The study highlights the feasibility of using IVIM for differentiating benign and malignant breast lesions.
  • Standardized IVIM analysis across different platforms can enhance diagnostic capabilities in breast MRI.