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

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

7.6K
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...
7.6K

You might also read

Related Articles

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

Sort by
Same author

microRNA-221 restricts human cytomegalovirus replication via promoting type I IFN production by targeting SOCS1/NF-κB pathway.

Cell cycle (Georgetown, Tex.)·2019
Same author

Hydrogen bonding derived self-healing polymer composites reinforced with amidation carbon fibers.

Nanotechnology·2019
Same author

Comparative analysis of the main active constituents from different parts of Leonurus japonicus Houtt. and from different regions in China by ultra-high performance liquid chromatography with triple quadrupole tandem mass spectrometry.

Journal of pharmaceutical and biomedical analysis·2019
Same author

The Comprehensive Evaluation of Safflowers in Different Producing Areas by Combined Analysis of Color, Chemical Compounds, and Biological Activity.

Molecules (Basel, Switzerland)·2019
Same author

The DNA-binding mechanism of the TCS response regulator ArlR from Staphylococcus aureus.

Journal of structural biology·2019
Same author

Two microporous Co<sup>II</sup>-MOFs with dual active sites for highly selective adsorption of CO<sub>2</sub>/CH<sub>4</sub> and CO<sub>2</sub>/N<sub>2</sub>.

Dalton transactions (Cambridge, England : 2003)·2019
Same journal

Amide proton transfer-weighted magnetic resonance imaging for predicting histopathology and biomarkers in rectal adenocarcinoma.

Quantitative imaging in medicine and surgery·2026
Same journal

Multimodality imaging for diagnosing and monitoring immunoglobulin G4-related coronary arteritis presenting as giant aneurysm: a case description.

Quantitative imaging in medicine and surgery·2026
Same journal

Investigation of the topological properties of brain structural and functional networks in patients with mild cognitive impairment.

Quantitative imaging in medicine and surgery·2026
Same journal

The critical role of transesophageal echocardiography in diagnosing carbon dioxide gas embolism: a case description and lessons learned.

Quantitative imaging in medicine and surgery·2026
Same journal

Impact of data augmentation size on deep learning-based third lumbar vertebra computed tomography skeletal muscle segmentation performance.

Quantitative imaging in medicine and surgery·2026
Same journal

Quantitative measurement of cutaneous neurofibromas in neurofibromatosis type 1 using a structured-light scanner.

Quantitative imaging in medicine and surgery·2026
See all related articles

Related Experiment Video

Updated: Sep 9, 2025

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

9.9K

Deep learning-based super-resolution method for projection image compression in radiotherapy.

Zhixing Chang1, Jiawen Shang1, Yuhan Fan1

  • 1Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Quantitative Imaging in Medicine and Surgery
|September 2, 2025
PubMed
Summary
This summary is machine-generated.

A deep learning (DL) super-resolution (SR) method significantly enhances compression ratios for radiotherapy projection images. This approach effectively compresses both 2D and 3D images, improving storage economy.

Keywords:
Cone-beam computed tomography (CBCT)compressionprojection imageradiotherapysuper-resolution (SR)

More Related Videos

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

2.9K
Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.5K

Related Experiment Videos

Last Updated: Sep 9, 2025

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

9.9K
Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

2.9K
Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.5K

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Data Compression

Background:

  • Cone-beam computed tomography (CBCT) is crucial for radiotherapy target verification.
  • CBCT generates massive projection image data, often abandoned due to storage limitations.
  • Economical storage of CBCT projection data is a significant challenge.

Purpose of the Study:

  • To investigate a deep learning (DL)-based super-resolution (SR) method for compressing CBCT projection images.
  • To evaluate the effectiveness of DL SR in conjunction with video codecs for image compression.
  • To assess the trade-off between compression ratio and image quality.

Main Methods:

  • Down-sampling high-resolution (HR) projection images to low-resolution (LR) for encoding.
  • Utilizing DL networks (CNN, ResNet, GAN) for up-sampling LR images back to HR.
  • Testing video coding-decoding (CODEC) algorithms: AVC, HEVC, and AV1.
  • Evaluating performance using compression ratio (CR), PSNR, VQM, and SSIM.

Main Results:

  • AV1 codec achieved the highest compression ratios across different down-sampling factors (DSF).
  • ResNet demonstrated superior restoration accuracy, maintaining high quality even at increased DSFs.
  • Compression ratio increased with DSF, with only modest degradation in restored image quality.

Conclusions:

  • DL-based SR models can further improve compression ratios beyond conventional video encoders.
  • This compression technique is effective for both 2D projection and 3D radiotherapy images.
  • The method offers a viable solution for economical storage of large imaging datasets in radiotherapy.