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

Reducing Line Loss01:18

Reducing Line Loss

180
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
180
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

7.1K
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...
7.1K
Downsampling01:20

Downsampling

201
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
201
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

124
Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
124
Deconvolution01:20

Deconvolution

205
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
205

You might also read

Related Articles

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

Sort by
Same author

Correction to "Indocyanine Green Aggregation-Induced Hypotonic Stress to Remodel Aloe Exosome-like Vesicles for Enhanced Tumor Penetration and Phototherapy".

ACS nano·2026
Same author

Potential Causal Relationships Between Circulating Micronutrient Levels and Multiple Neuroimmune Diseases: A Genetic Association Analysis.

Brain and behavior·2025
Same author

The relationship between catheter-related bloodstream infection and multi-drug resistant bacteria: a five-year retrospective study.

BMC infectious diseases·2025
Same author

Role of low-dose cadmium exposure to the pathogenesis of gestational diabetes mellitus.

Environmental pollution (Barking, Essex : 1987)·2025
Same author

Indocyanine Green Aggregation-Induced Hypotonic Stress to Remodel Aloe Exosome-like Vesicles for Enhanced Tumor Penetration and Phototherapy.

ACS nano·2025
Same author

Diagnosis assistant for liver cancer utilizing a large language model with three types of knowledge.

Physics in medicine and biology·2025
Same journal

Accurate Segmentation and Three-dimensional Reconstruction Algorithm of Spinal Cord Injury Lesions Based on Multimodal Magnetic Resonance Imaging.

Current medical imaging·2026
Same journal

A Comprehensive Review of Radiomics in Pulmonary Nodule Management: Clinical Applications and Standardization Dilemmas.

Current medical imaging·2026
Same journal

The Value of a Predictive Model Based on Multimodal Ultrasound Imaging Biomarkers Combined with Clinical Features in the Diagnosis of Thyroid Nodules.

Current medical imaging·2026
Same journal

The Prognostic and Mutational Characteristics of Multiple Early-stage Lung Cancers Manifesting as Subsolid Nodules.

Current medical imaging·2026
Same journal

Dual-Database Bibliometric Analysis Combined with Gephi-Based Network Visualization of Artificial Intelligence Applications in the Identification and Diagnosis of Thyroid Space-Occupying Lesions.

Current medical imaging·2026
Same journal

An Efficient and Cohesive System for Enhanced Accuracy in Malignant Brain Tumor Diagnosis.

Current medical imaging·2026
See all related articles

Related Experiment Video

Updated: Jul 29, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

591

A lightweight super-resolution network with skip-connections.

Xuzhou Wu1, Shi Lu1, Jirang Sun2

  • 1Graduate School at Shenzhen, Tsinghua University, Shenzhen, China.

Current Medical Imaging
|May 23, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a lightweight algorithm to enhance low-resolution MRI images, improving diagnostic accuracy in remote areas lacking advanced MRI scanners. The method offers high performance with minimal computational resources, aiding clinical decisions.

Keywords:
Image super-resolutionLESRCNNLightweight networkMRI imagesNuclear magnetic resonance imageSPSRSRGAN

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

448
Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons
14:02

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Published on: October 31, 2020

5.8K

Related Experiment Videos

Last Updated: Jul 29, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

591
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

448
Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons
14:02

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Published on: October 31, 2020

5.8K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Low-resolution MRI images are common in remote areas due to limited scanner availability.
  • This limitation hinders accurate diagnosis and treatment planning.
  • Developing accessible image enhancement solutions is crucial for equitable healthcare.

Purpose of the Study:

  • To develop a lightweight super-resolution algorithm for MRI images.
  • To improve the resolution of MRI scans obtained from low-field intensity scanners.
  • To enable accurate diagnoses in resource-limited clinical settings.

Main Methods:

  • Compared super-resolution algorithms including SRGAN, SPSR, and LESRCNN.
  • Modified the LESRCNN network by incorporating a global skip connection.
  • Evaluated performance using metrics like SSMI, PSNR, PI, and LPIPS.

Main Results:

  • The proposed algorithm demonstrated improved SSMI, PSNR, PI, and LPIPS compared to LESRCNN.
  • The algorithm is computationally efficient with a small parameter count and low complexity.
  • Clinical evaluation by MRI doctors confirmed significant improvements and potential for remote use.

Conclusions:

  • The developed algorithm effectively reconstructs high-resolution MRI images from low-resolution inputs.
  • Its lightweight nature makes it suitable for deployment in remote hospitals with limited computing resources.
  • The algorithm holds significant clinical value, aiding diagnoses and saving patient time.