Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Magnetic Resonance Imaging

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

Self-assembled chamber-like cardiac organoids for modeling cardiac chamber formation and cardiotoxicity assessment.

Nature communications·2026

Same author

Evaluation of Third-Order Motion-Compensated Cardiac Diffusion Tensor Imaging Across Cardiac Phases Using an Ultra-High-Performance Clinical Scanner.

Magnetic resonance in medicine·2026

Same author

From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstruction.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

Explicit differentiable slicing and global deformation for cardiac mesh reconstruction.

Medical image analysis·2026

Same author

Toward Modality- and Sampling-Universal Learning Strategies for Accelerating Cardiovascular Imaging: Summary of the CMRxRecon2024 Challenge.

IEEE transactions on medical imaging·2025

Same author

Steady-state free precession for T<sub>2</sub>* relaxometry: All echoes in every readout with k-space aliasing.

Magnetic resonance in medicine·2025

Search research articles

Related Experiment Video

Updated: Apr 22, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Cyclic Self-Supervised Diffusion for Ultra Low-Field to High-Field MRI Synthesis.

Zhenxuan Zhang, Peiyuan Jing, Zi Wang

IEEE Transactions on Medical Imaging

|April 20, 2026

Summary

Synthesizing high-field MRI from low-field data improves accessibility. Our CSS-Diff framework enhances anatomical detail and image quality, bridging the clinical fidelity gap for better MRI synthesis.

More Related Videos

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

Published on: December 9, 2010

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

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

Published on: June 13, 2025

Related Experiment Videos

Last Updated: Apr 22, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

Published on: December 9, 2010

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

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

Published on: June 13, 2025

Area of Science:

Medical Imaging
Artificial Intelligence
Biomedical Engineering

Background:

Low-field MRI offers cost and safety benefits but yields low-resolution images.
Synthesizing high-field MRI from low-field data is crucial but faces a clinical fidelity gap.
Existing methods struggle with anatomical fidelity, fine structural details, and image contrast differences.

Purpose of the Study:

To develop a novel framework for synthesizing high-field MRI from low-field data.
To address the clinical fidelity gap in MRI synthesis.
To improve anatomical accuracy and structural detail in synthesized MRI images.

Main Methods:

Proposed a cyclic self-supervised diffusion (CSS-Diff) framework.
Incorporated cycle-consistent constraints for anatomical preservation.
Introduced slice-wise gap perception and local structure correction networks.

Main Results:

Achieved state-of-the-art performance in cross-field MRI synthesis (PSNR, SSIM, LPIPS).
Significantly improved preservation of fine-grained anatomical structures (e.g., white matter, cortex).
Demonstrated quantitative reliability and anatomical consistency in synthesized images.

Conclusions:

CSS-Diff effectively synthesizes high-field MRI from low-field data.
The framework bridges the clinical fidelity gap, enhancing image quality and anatomical accuracy.
The method holds potential for reducing reliance on costly MRI acquisitions and expanding data availability.