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 IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

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,...

You might also read

Related Articles

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

Sort by
Same author

Complex Deep Neural Networks for Denoising Ultra-Fast Submillimeter T2*-weighted Imaging and Quantitative Susceptibility Mapping.

European journal of radiology artificial intelligence·2026
Same authorSame journal

Generative MR Multitasking With Complex-Harmonic Cardiac Encoding: Bridging the Gap Between Gated Imaging and Real-Time Imaging.

Magnetic resonance in medicine·2026
Same author

A Porcine Model of Intervertebral Disc Injury Recapitulates Human Discogenic Pain Via Notochordal Cell Loss and Pain-Inducing Nucleus Pulposus Cell Emergence.

JOR spine·2026
Same author

Coffee Consumption and Improved Liver Outcomes: Clinical, Imaging, and Proteomic Evidence From the UK Biobank.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association·2026
Same author

Superficial Siderosis and a Spinal Cord Cleft.

JAMA neurology·2026
Same author

The "Brain's Traffic Map" Reveals Neural Pathways Linked to Coronary Microvascular Dysfunction in Women.

Brain and behavior·2026

Related Experiment Video

Updated: Jun 8, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.9K

Multiparametric mapping in the brain from conventional contrast-weighted images using deep learning.

Shihan Qiu1,2, Yuhua Chen1,2, Sen Ma1

  • 1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.

Magnetic Resonance in Medicine
|August 10, 2021
PubMed
Summary

A new deep learning method accurately estimates brain T1 and T2 maps from standard MRI scans. This advance allows for simultaneous qualitative and quantitative imaging without changing clinical protocols.

Keywords:
braindeep learningmagnetic resonance imaging (MRI)multiparametric mappingquantitative imaging

More Related Videos

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.7K
Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping
09:55

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

1.5K

Related Experiment Videos

Last Updated: Jun 8, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.9K
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.7K
Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping
09:55

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

1.5K

Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Quantitative magnetic resonance imaging (MRI) provides valuable diagnostic information but often requires specialized sequences.
  • Conventional contrast-weighted MRI sequences are widely used in clinical practice.
  • Estimating quantitative parameters like T1 and T2 relaxation times from conventional images could enhance diagnostic capabilities.

Purpose of the Study:

  • To develop and validate a deep learning-based method for simultaneously quantifying T1 and T2 relaxation times in the brain.
  • To utilize conventional contrast-weighted MRI images as input for the deep learning model.
  • To enable simultaneous qualitative and quantitative MRI analysis without altering standard clinical imaging protocols.

Main Methods:

  • A U-Net based convolutional neural network was designed to estimate T1 and T2 maps.
  • The network was trained using conventional T1-weighted (MPRAGE, GRE) and T2-weighted (FLAIR) images as input.
  • Reference T1 and T2 maps were generated using a specialized MR Multitasking sequence in 18 subjects.
  • Six-fold cross-validation was employed to assess the accuracy of the deep learning model against reference maps.

Main Results:

  • The deep learning-derived T1 and T2 maps demonstrated high fidelity, preserving brain structures and image contrast.
  • Excellent image quality was achieved, with peak signal-to-noise ratio >32 dB and structural similarity index >0.97.
  • Mean absolute errors for T1 and T2 maps were 52.7 ms (5.1%) and 5.4 ms (7.1%), respectively, in brain parenchyma.
  • Region of interest analysis showed strong agreement between deep learning estimates and reference values, with mean differences <1% and limits of agreement within 5-10%.

Conclusions:

  • A novel deep learning technique enables accurate estimation of brain T1 and T2 maps from conventional MRI scans.
  • This method facilitates simultaneous qualitative and quantitative MRI analysis.
  • The developed approach integrates seamlessly into existing clinical workflows, enhancing diagnostic potential without protocol modification.