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

5.5K
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...
5.5K

You might also read

Related Articles

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

Sort by
Same author

CE-MRA-FLOWnet: Fast and Accurate Generative AI-Based Aortic Hemodynamic Mapping from Contrast-Enhanced MRA.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance·2026
Same author

Restoration of 3-dimensional aortic hemodynamics after the Ross procedure for unicuspid aortic valve disease using 4-dimensional flow magnetic resonance imaging.

JTCVS structural and endovascular·2026
Same author

Corrigendum to "4D Flow cardiovascular magnetic resonance consensus statement: 2023 update" [Journal of Cardiovascular Magnetic Resonance 25 (2023) 40].

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance·2026
Same author

Fully Automated Quantification of Functional Small Airway Disease at Inspiratory and Expiratory Chest CT Using Deep Learning.

Radiology. Cardiothoracic imaging·2026
Same author

Four centuries of commercial whaling eroded 11,000 years of population stability in bowhead whales.

Cell·2026
Same author

ACR Appropriateness Criteria® Evaluation of Cardiac Masses.

Journal of the American College of Radiology : JACR·2026

Related Experiment Video

Updated: Aug 23, 2025

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
06:56

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

2.5K

Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI.

Maurice Pradella1,2, Michael B Scott3, Muhammad Omer4

  • 1Department of Radiology, Northwestern University, 737 N Michigan Ave, Suite 1600, Chicago, IL, 60611, USA. maurice.pradella@northwestern.edu.

European Radiology
|October 28, 2022
PubMed
Summary

Deep learning (DL) for 2D-CINE-PC-MRI offers accurate, automated blood flow quantification at the sinotubular junction and pulmonary artery. This advanced technique achieves expert-level results instantaneously, improving clinical workflow.

Keywords:
Blood flowBlood flow velocityDeep learningMagnetic resonance imagingPulmonary artery

More Related Videos

Phase Contrast Magnetic Resonance Imaging in the Rat Common Carotid Artery
07:02

Phase Contrast Magnetic Resonance Imaging in the Rat Common Carotid Artery

Published on: September 5, 2018

9.6K
In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging
11:16

In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging

Published on: February 25, 2022

3.4K

Related Experiment Videos

Last Updated: Aug 23, 2025

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
06:56

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

2.5K
Phase Contrast Magnetic Resonance Imaging in the Rat Common Carotid Artery
07:02

Phase Contrast Magnetic Resonance Imaging in the Rat Common Carotid Artery

Published on: September 5, 2018

9.6K
In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging
11:16

In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging

Published on: February 25, 2022

3.4K

Area of Science:

  • Cardiovascular Imaging
  • Medical Artificial Intelligence
  • Radiology

Background:

  • Time-resolved, 2D-phase-contrast MRI (2D-CINE-PC-MRI) is crucial for in vivo blood flow analysis.
  • Accurate vessel contour delineation (VCD) is essential for reliable 2D-CINE-PC-MRI results.
  • Manual analysis (MA) and semi-automated methods can be time-consuming and prone to variability.

Purpose of the Study:

  • To evaluate a fully-automated deep learning (DL) application for VCD and blood flow quantification.
  • To compare the performance of DL analysis against manual analysis (MA) and corrected semi-automated analysis (corSAA).
  • To assess the accuracy and efficiency of DL in analyzing 2D-CINE-PC-MRI data.

Main Methods:

  • 97 patients with 2D-CINE-PC-MRI at the sinotubular junction (STJ) and 28 at the main pulmonary artery (PA) were included.
  • A cardiovascular radiologist performed MA (reference) and corSAA; DL performed automated VCD and flow quantification (net flow [NF] and peak velocity [PV]).
  • Contour accuracy was assessed using Dice similarity coefficients (DSC); discrepant cases were reviewed.

Main Results:

  • DL was successfully applied to 97% of imaging series, demonstrating good to excellent performance (mean DSC: 0.91 at STJ, 0.85 at PA).
  • Flow quantification showed similar net flow between DL and human assessments (p > 0.05).
  • DL analysis was accurate in 93.4% of cases, with instantaneous results compared to manual assessments.

Conclusions:

  • Fully-automated DL analysis of 2D-CINE-PC-MRI provides expert-level flow quantification at STJ and PA in over 93% of cases.
  • DL offers instantaneous results, significantly improving efficiency over manual methods.
  • The evaluated DL tool demonstrates usability and potential for integration into daily clinical practice.