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

Computed Tomography01:10

Computed Tomography

4.7K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
4.7K
Positron Emission Tomography01:29

Positron Emission Tomography

4.4K
Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
4.4K
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

57
Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
57
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

385
Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
385
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

35
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
35

You might also read

Related Articles

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

Sort by
Same author

Completion of Metal-Damaged Traces Based on Deep Learning in Sinogram Domain for Metal Artifacts Reduction in CT Images.

Sensors (Basel, Switzerland)·2021
Same author

Direct Tissue Mass Spectrometry Imaging by Atmospheric Pressure UV-Laser Desorption Plasma Postionization.

Journal of the American Society for Mass Spectrometry·2020
Same author

A novel secretagogin/ATF4 pathway is involved in oxidized LDL-induced endoplasmic reticulum stress and islet β-cell apoptosis.

Acta biochimica et biophysica Sinica·2020
Same author

Intravenous Thrombolysis before Thrombectomy may Increase the Incidence of Intracranial Hemorrhage inTreating Carotid T Occlusion.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association·2020
Same author

Safety and efficacy of traditional Chinese medicine, Qiaoshao formula, combined with dapoxetine in the treatment of premature ejaculation: An open-label, real-life, retrospective multicentre study in Chinese men.

Andrologia·2020
Same author

New insights into the interplay between miRNAs and autophagy in the aging of intervertebral discs.

Ageing research reviews·2020

Related Experiment Video

Updated: Aug 12, 2025

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

473

STEDNet: Swin transformer-based encoder-decoder network for noise reduction in low-dose CT.

Linlin Zhu1, Yu Han1, Xiaoqi Xi1

  • 1Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China.

Medical Physics
|January 28, 2023
PubMed
Summary

This study introduces a novel Swin Transformer-based network for low-dose computed tomography (LDCT) image denoising. The method effectively suppresses noise and artifacts while preserving crucial image details for improved diagnostic accuracy.

Keywords:
Swin transformerdeep learningencoder-decoderlow-dose CT

More Related Videos

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

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

Published on: April 21, 2023

1.9K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K

Related Experiment Videos

Last Updated: Aug 12, 2025

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

473
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

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

Published on: April 21, 2023

1.9K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Image Processing

Background:

  • Low-dose computed tomography (LDCT) is vital for clinical diagnosis, but radiation noise degrades image quality.
  • Noise, artifacts, and high-frequency components in LDCT images hinder accurate diagnosis.
  • Transformers offer potential for capturing global context and improving feature extraction in image analysis.

Purpose of the Study:

  • To develop an advanced network for suppressing noise and artifacts in LDCT images.
  • To enhance the retention of detailed information and improve noise suppression performance.
  • To leverage Transformer capabilities for more powerful feature extraction in medical imaging.

Main Methods:

  • Proposed a Swin Transformer-based network with separate noise extraction and removal sub-networks.
  • Employed a coarse extraction network with full convolution for high-frequency features.
  • Utilized a Swin Transformer encoder-decoder with skip connections for global feature fusion and multi-scale feature extraction.
  • Implemented a combined L1 and MS-SSIM loss constraint for stable and effective denoising.

Main Results:

  • The proposed STEDNet method demonstrated superior denoising performance compared to DnCNN, RED-CNN, CBDNet, and TSCN.
  • Achieved significant reductions in Root Mean Square Error (RMSE) and improvements in Peak Signal-to-Noise Ratio (PSNR) on clinical datasets.
  • Effectively removed noise while preserving image structures, edges, and textures, closely reconstructing near standard-dose CT images.

Conclusions:

  • The end-to-end trained LDCT image denoising algorithm effectively enhances CT image diagnostic performance.
  • The method successfully addresses noise and artifacts while maintaining the integrity of tissue structure and pathological information.
  • Offers superior quantitative and qualitative denoising effects compared to existing algorithms.