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

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...

You might also read

Related Articles

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

Sort by
Same author

Letter to the Editor: comments on the diagnostic value of ADC texture analysis in PI-RADS 5 lesions.

Diagnostic and interventional radiology (Ankara, Turkey)·2026
Same author

A Perspective on Quantitative Performance Metrics for Large Language Models in Radiology Report Analysis.

AJR. American journal of roentgenology·2026
Same author

Diagnostic accuracy of artificial intelligence models in childhood exanthematous diseases: a comparative analysis against clinical diagnosis.

European journal of pediatrics·2025
Same author

Preoperative semi-automatic segmentation in incus defects.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery·2025
Same author

Emergency bronchial artery embolization using n-2-butyl-cyanoacrylate: a safe and effective solution for massive hemoptysis.

BMC pulmonary medicine·2025
Same author

Can LLMs simplify operative notes? A comparative analysis in otorhinolaryngology.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery·2025

Related Experiment Video

Updated: Jul 10, 2026

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging
06:44

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Published on: June 7, 2020

BrainGAN Framework for Generating Synthetic Contrast-Enhanced Multisequence MRI in Brain Metastases.

Merve Solak1, Murat Tören2, Berkutay Asan2

  • 1Department of Radiology, Recep Tayyip Erdogan University Training and Research Hospital, Rize, Turkey.

Journal of Imaging Informatics in Medicine
|July 9, 2026
PubMed
Summary

This study demonstrates the technical feasibility of generating synthetic contrast-enhanced MRI images from noncontrast scans using the BrainGAN framework. This AI approach shows promise for neuro-oncologic imaging, potentially reducing reliance on gadolinium-based contrast agents.

Keywords:
Brain metastasesBrainGANGadolinium-based contrast agentsGenerative adversarial networksNoncontrast MRISynthetic contrast-enhanced MRI

More Related Videos

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring
17:16

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

Published on: December 9, 2010

Related Experiment Videos

Last Updated: Jul 10, 2026

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging
06:44

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Published on: June 7, 2020

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring
17:16

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

Published on: December 9, 2010

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Neuro-oncology

Background:

  • Contrast-enhanced MRI (CE-MRI) is standard for evaluating brain metastases (BMs).
  • Concerns about gadolinium-based contrast agents necessitate alternative imaging strategies.
  • Developing synthetic CE-MRI could improve safety and accessibility.

Purpose of the Study:

  • To investigate the technical feasibility of generating synthetic contrast-enhanced T1 (CE-T1) and FLAIR (CE-FLAIR) images.
  • To utilize the BrainGAN generative adversarial network (GAN) framework for image synthesis from noncontrast MRI.
  • To assess the performance of different GAN architectures in creating realistic synthetic contrast-enhanced images.

Main Methods:

  • Retrospective analysis of 83 patients with T1/CE-T1 pairs and 100 patients with T2/CE-FLAIR pairs.
  • Training four GAN architectures (Pix2PixHD, CycleGAN, C-CycleGAN, CGAN) within the BrainGAN framework.
  • Evaluating image synthesis using quantitative metrics (MSE, SSIM, PSNR, RMSE) and a qualitative visual Turing test by neuroradiologists.

Main Results:

  • The BrainGAN framework successfully synthesized CE-T1 and CE-FLAIR images from noncontrast MRI.
  • Pix2PixHD architecture demonstrated the highest performance (T1: SSIM 0.90, PSNR 29.2 dB; T2: SSIM 0.90, PSNR 27.3 dB).
  • The visual Turing test achieved 61.4% accuracy, indicating reproducible qualitative characteristics.

Conclusions:

  • The BrainGAN framework is technically feasible for generating synthetic CE-T1 and CE-FLAIR images.
  • This AI-driven approach offers consistent quantitative performance and reproducible qualitative results.
  • Synthetic contrast generation shows potential for advancing neuro-oncologic imaging and reducing contrast agent risks.