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

An MRI Atlas of the Human Fetal Brain: Reference and Segmentation Tools for Fetal Brain MRI Analysis.

Scientific data·2026

Same author

Deep medullary vein thrombosis in neonates: a retrospective study.

Journal of perinatology : official journal of the California Perinatal Association·2026

Same author

From Velasco to NAUTILUS: an evolution of centromedian nucleus targeting in epilepsy.

Journal of neurosurgery·2026

Same author

Comparison of dynamic contrast-enhanced MRI versus MAG-3 scintigraphy for differential renal function assessment in pediatric patients.

Pediatric radiology·2026

Same author

ACR Appropriateness Criteria® Hydrocephalus-Child.

Journal of the American College of Radiology : JACR·2026

Same author

Strategies for radiology faculty recruitment and retention in a competitive market: implications for pediatric radiology.

Pediatric radiology·2026

Same journal

Integrating Electronic Health Records and Large Language Models for Coarse-to-Fine Hybrid Disease Prediction.

Health data science·2026

Same journal

Electronic Health Record-Based Machine Learning Model for Predicting Disease Activity in Patients with Rheumatoid Arthritis.

Health data science·2026

Same journal

Exploring the Relationship between Dietary Intake and Clinical Outcomes in Peritoneal Dialysis Patients Stratified by Serum Albumin Levels: A 12-Year Follow-Up Using Fine-Grained Electronic Medical Records Data.

Health data science·2026

Same journal

Health in a Digital Age: From Determinants to Dynamic Systems.

Health data science·2026

Same journal

Digital and AI-Empowered Health Elements: An Integrated Pathway to Advancing Health.

Health data science·2026

Same journal

ECGomics: An Open Platform for AI-ECG Digital Biomarker Discovery.

Health data science·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 16, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Unsupervised Transformer Learning for Rapid and High-Quality MRI Data Acquisition.

Yao Sui^1,2,3, Onur Afacan^4,5, Camilo Jaimes^4,6

¹National Institute of Health Data Science, Peking University, Beijing, China.

Health Data Science

|October 6, 2025

Summary

This summary is machine-generated.

This study introduces a novel transformer-based super-resolution method for Magnetic Resonance Imaging (MRI). This approach significantly enhances image quality and reduces acquisition time, improving diagnostic capabilities.

More Related Videos

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods

Published on: September 6, 2024

Related Experiment Videos

Last Updated: Jan 16, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods

Published on: September 6, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Biomedical Engineering

Background:

Magnetic Resonance Imaging (MRI) is crucial for scientific research and clinical diagnostics.
High-quality MRI data acquisition is essential.
Current super-resolution methods using convolutional neural networks have limitations in capturing extensive spatial dependencies.

Purpose of the Study:

To develop a novel super-resolution methodology for rapid, high-quality MRI data acquisition.
To overcome the limitations of localized convolutional layers in capturing long-range spatial dependencies.
To enable unsupervised learning for subject-specific super-resolution tasks.

Main Methods:

Developed an innovative architecture utilizing transformers to exploit long-range spatial dependencies.
Implemented an unsupervised learning framework tailored for super-resolution.
Validated the approach using simulated and clinical data from a 3-T MRI system.

Main Results:

Achieved T2 contrast images at 500 μm isotropic spatial resolution in 4 minutes.
Improved signal-to-noise ratio by 13.23% and contrast-to-noise ratio by 18.45% compared to leading methods.
Demonstrated substantial improvement in super-resolution reconstruction by incorporating long-range spatial dependencies.

Conclusions:

Transformer-based super-resolution significantly enhances MRI data quality.
The proposed method allows for high-quality MRI acquisition with reduced imaging time.
This advancement has the potential to improve both scientific research and clinical diagnostics.