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

Super-resolution Fluorescence Microscopy

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...

You might also read

Related Articles

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

Sort by

Same author

Changes in body composition in early breast cancer patients treated with aromatase inhibitors.

Journal of endocrinological investigation·2024

Same author

Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods.

Brain & spine·2022

Same author

Vaccination for monkeypox prevention in persons with high-risk sexual behaviours to control on-going outbreak of monkeypox virus clade 3.

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases·2022

Same author

Thyroid dysfunction in COVID-19 patients.

Journal of endocrinological investigation·2021

Same author

Characterization of displacement forces and image artifacts in the presence of passive medical implants in low-field (<100 mT) permanent magnet-based MRI systems, and comparisons with clinical MRI systems.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)·2021

Same author

Performance of the Emprint and Amica Microwave Ablation Systems in ex vivo Porcine Livers: Sphericity and Reproducibility Versus Size.

Cardiovascular and interventional radiology·2021

Same journal

Correction: A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms.

Scientific reports·2026

Same journal

Poly(bromophenol blue)/CoSn(OH)<sub>6</sub> cubic particles modified pencil graphite electrode for electrochemical determination of diphenhydramine.

Scientific reports·2026

Same journal

Dietary Chlorella, Spirulina, and acidifier modulate jejunal cytokine-related gene expression in broiler chickens.

Scientific reports·2026

Same journal

Perceived physical activity barriers in university students: associations with fatigue and eating behaviours.

Scientific reports·2026

Same journal

Refuge limitation structures habitat use in agricultural landscapes: evidence from Sunda pangolins.

Scientific reports·2026

Same journal

Lightweight stateless transaction verification with outsourced witness updates for UTXO blockchains.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 26, 2025

High-resolution Structural Magnetic Resonance Imaging of the Human Subcortex In Vivo and Postmortem

High-resolution Structural Magnetic Resonance Imaging of the Human Subcortex In Vivo and Postmortem

Published on: December 30, 2015

Deep learning-based single image super-resolution for low-field MR brain images.

M L de Leeuw den Bouter¹, G Ippolito², T P A O'Reilly³

¹Delft Institute of Applied Mathematics, Delft University of Technology, Delft, The Netherlands. M.L.deLeeuwdenBouter-1@tudelft.nl.

Scientific Reports

|April 17, 2022

Summary

This summary is machine-generated.

Deep learning enhances low-field MRI images, improving resolution for wider accessibility. This method transforms low-resolution scans into high-resolution ones, recovering crucial details.

More Related Videos

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published on: August 1, 2022

Author Spotlight: Advancing 3D Cytoarchitecture Analysis - Rapid Volumetric Reconstruction of the Human Brain

Author Spotlight: Advancing 3D Cytoarchitecture Analysis - Rapid Volumetric Reconstruction of the Human Brain

Published on: January 26, 2024

Related Experiment Videos

Last Updated: Sep 26, 2025

High-resolution Structural Magnetic Resonance Imaging of the Human Subcortex In Vivo and Postmortem

High-resolution Structural Magnetic Resonance Imaging of the Human Subcortex In Vivo and Postmortem

Published on: December 30, 2015

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published on: August 1, 2022

Author Spotlight: Advancing 3D Cytoarchitecture Analysis - Rapid Volumetric Reconstruction of the Human Brain

Author Spotlight: Advancing 3D Cytoarchitecture Analysis - Rapid Volumetric Reconstruction of the Human Brain

Published on: January 26, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Biomedical Engineering

Background:

Low-field MRI scanners offer cost-effective alternatives to high-field systems, increasing global accessibility.
A key limitation of low-field MRI is the lower image resolution due to reduced signal-to-noise ratios.

Purpose of the Study:

To develop a deep learning method for enhancing the resolution of low-field MRI images.
To investigate the potential of artificial intelligence in improving the diagnostic quality of low-field MRI.

Main Methods:

A convolutional neural network (CNN) was trained for single image super-resolution.
The CNN was trained using pairs of low-resolution, noisy MR images and their corresponding high-resolution, noise-free counterparts from the NYU fastMRI database.
The trained network was applied to reconstruct high-resolution images from low-field MRI data.

Main Results:

The deep learning approach successfully transformed low-resolution low-field MR images into high-resolution ones.
The reconstructed images exhibited sharpness, with significant recovery of high-frequency components.
The method effectively addressed the resolution limitations inherent in low-field MRI.

Conclusions:

Deep learning-based super-resolution is a promising technique for improving low-field MRI image quality.
This approach can significantly enhance the diagnostic utility of more accessible low-field MRI scanners.
The findings suggest a pathway to more affordable and detailed medical imaging worldwide.