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

Amplatzer Plug Embolization for Iatrogenic Aortic Arch Catheterization: A Rescue Technique.

Cardiovascular and interventional radiology·2026
Same author

Differentiating Hemorrhage and Contrast Extravasation After Mechanical Thrombectomy Using Virtual Non-Contrast Photon-Counting CT.

Clinical neuroradiology·2026
Same author

Dynamic uncertainty-level assessment framework for real-time needle tracking in CT-guided surgical environments.

International journal of computer assisted radiology and surgery·2026
Same author

Bone Mineral Density Does Not Predict Overall Survival in Patients with Advanced Hepatocellular Carcinoma: A Subanalysis of the SORAMIC Trial.

Digestive diseases (Basel, Switzerland)·2026
Same author

Real-time marker-less needle tracking for CT-guided interventions using multiple RGB cameras.

International journal of computer assisted radiology and surgery·2026
Same author

Detection rate and mutational landscape in extracranial arteriovenous malformations: a cohort study.

BMC medicine·2026

Related Experiment Video

Updated: Jul 23, 2025

Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI
05:37

Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI

Published on: October 20, 2023

1.4K

Transfer-learning is a key ingredient to fast deep learning-based 4D liver MRI reconstruction.

Gino Gulamhussene1, Marko Rak2, Oleksii Bashkanov2

  • 1Otto-von-Guericke University Magdeburg, Faculty of Computer Science, 39106, Magdeburg, Germany. gino.gulamhussene@ovgu.de.

Scientific Reports
|July 11, 2023
PubMed
Summary

Transfer learning and ensembling significantly improve deep learning-based 4D MRI reconstruction for organ motion. This approach reduces acquisition time and enhances image quality, making 4D MRI more clinically viable.

More Related Videos

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

546
Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
06:09

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

1.2K

Related Experiment Videos

Last Updated: Jul 23, 2025

Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI
05:37

Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI

Published on: October 20, 2023

1.4K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

546
Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
06:09

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

1.2K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Biomedical Engineering

Background:

  • Time-resolved volumetric 4D MRI is crucial for image-guided interventions, but current methods struggle with organ motion due to limitations in resolution and speed.
  • Deep learning (DL) offers potential solutions but faces challenges with domain shift, impacting real-world applicability.

Purpose of the Study:

  • To evaluate transfer learning (TL) combined with ensembling as a strategy to overcome domain shift in DL-based 4D MRI reconstruction.
  • To improve the temporal and spatial resolution and reduce reconstruction time for 4D MRI in interventional settings.

Main Methods:

  • Four DL approaches were compared: source-pretrained, target-trained from scratch, fine-tuned, and an ensemble of fine-tuned models.
  • A dataset was split into 16 source and 4 target domain subjects to assess performance.
  • The study focused on 4D organ motion models, particularly for the liver.

Main Results:

  • The ensemble of fine-tuned models (N=10) showed significant improvements (P < 0.001) compared to directly learned models.
  • Root mean squared error (RMSE) improved by up to 12%, and mean displacement (MDISP) by up to 17.5%.
  • The benefits of TL + Ens were more pronounced with smaller target domain datasets.

Conclusions:

  • Transfer learning combined with ensembling effectively mitigates domain shift in 4D MRI reconstruction.
  • This strategy significantly reduces prior acquisition time and enhances reconstruction quality.
  • TL + Ens is a key advancement for clinical feasibility of 4D MRI in tracking organ motion.