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

Surgical Video Understanding with Alignment-Preserving Temporal Adaptation and Action Triplet Text Alignment.

Bioengineering (Basel, Switzerland)·2026

Same author

For ultra-high dose rate carbon-ion irradiation, comparable beam parameters induce the equivalent cell sparing (FLASH) effect.

Journal of radiation research·2026

Same author

Minimization of Geometric Uncertainties and Setup Errors Improves Tumor Control for Intracranial Stereotactic Radiosurgery.

Advances in radiation oncology·2026

Same author

Evaluation of the efficacy of plan adaptation in stereotactic body proton therapy for pancreatic cancer.

Journal of applied clinical medical physics·2026

Same author

Efficacy and safety of carbon-ion radiotherapy for large hepatocellular carcinoma (diameter ≥ 10 cm).

BMC cancer·2026

Same author

Hokkaido phase II study of photo-immunotherapy combination with ASP-1929 for recurrent or persistent EGFR-positive vulvar, vaginal, and cervical cancers not amenable to further radiotherapy (HICARi study).

Journal of gynecologic oncology·2026

Same journal

Epidemiological characteristics of amebiasis in Japan from 2001 to 2022.

PloS one·2026

Same journal

Longitudinal associations of academic stress with eating related patterns, nutrition, somatic indicators, and depressive symptoms in university students: A study protocol.

PloS one·2026

Same journal

Pollution removal efficiency enhancement by agricultural biomass additions in constructed wetlands: A framework integrating meta-analysis with explainable machine learning.

PloS one·2026

Same journal

Insulation failure mapping on power transformer bushing using FRA and electrostatic simulation.

PloS one·2026

Same journal

Enhancing medical Q&A systems with multimodal knowledge graphs and dual-layer attention mechanisms.

PloS one·2026

Same journal

UAMP: Consistent video object segmentation with uncertainty-aware memory propagation.

PloS one·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 15, 2025

Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy

Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy

Published on: March 28, 2025

Prostatic urinary tract visualization with super-resolution deep learning models.

Takaaki Yoshimura^1,2, Kentaro Nishioka³, Takayuki Hashimoto³

¹Department of Health Sciences and Technology, Faculty of Health Sciences, Hokkaido University, Sapporo, Japan.

|January 6, 2023

Summary

This summary is machine-generated.

Super-resolution (SR) deep learning models enhance prostatic urinary tract visibility on post-urination MRI (PU-MRI) for urethra-sparing radiation therapy. The residual dense network (RDN) model showed the most promising results for improving visualization and reducing urinary side effects.

More Related Videos

Ultrasonography of the Adult Male Urinary Tract for Urinary Functional Testing

Ultrasonography of the Adult Male Urinary Tract for Urinary Functional Testing

Published on: August 14, 2019

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

Related Experiment Videos

Last Updated: Aug 15, 2025

Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy

Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy

Published on: March 28, 2025

Ultrasonography of the Adult Male Urinary Tract for Urinary Functional Testing

Ultrasonography of the Adult Male Urinary Tract for Urinary Functional Testing

Published on: August 14, 2019

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

Area of Science:

Medical Imaging
Radiation Oncology
Artificial Intelligence

Background:

Accurate visualization of the prostatic urinary tract is crucial for minimizing urinary side effects in urethra-sparing radiation therapy.
Post-urination magnetic resonance imaging (PU-MRI) offers a catheter-free method for visualizing the prostatic urinary tract.

Purpose of the Study:

To evaluate the effectiveness of combining PU-MRI with super-resolution (SR) deep learning models for improving prostatic urinary tract visibility.
To compare the performance of four different SR deep learning models (EDSR, WDSR, SRGAN, RDN) in enhancing PU-MRI images.

Main Methods:

Thirty patients undergoing proton therapy received non-contrast, high-resolution 2D T2-weighted turbo spin-echo PU-MRI.
Four SR deep learning models (EDSR, WDSR, SRGAN, RDN) were applied to the PU-MRI images.
Quantitative assessment using complex wavelet structural similarity index measure (CW-SSIM) and subjective evaluation by radiation oncologists.

Main Results:

All SR models achieved high quantitative similarity scores (CW-SSIM > 99.30%).
Subjective evaluation showed that the residual dense network (RDN) model significantly improved prostatic urinary tract visibility compared to standard PU-MRI.
RDN achieved the highest visibility scores (4.37 and 3.73) with strong inter-operator reliability (k = 0.93).

Conclusions:

Super-resolution deep learning models, particularly RDN, can effectively enhance prostatic urinary tract visibility on PU-MRI.
This enhancement has the potential to improve treatment planning and reduce urinary side effects in radiation therapy.
The RDN model provides results comparable to original images while subjectively improving visualization.