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

6.4K
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...
6.4K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

66
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...
66
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

478
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...
478
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

80
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...
80
Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

138
Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
138

You might also read

Related Articles

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

Sort by
Same author

Latent Code Predictor for Accelerating Disparity Estimation in Stereo-Endoscopic Surface Reconstruction.

Sensors (Basel, Switzerland)·2026
Same author

Correction: Wang et al. Adaptive Neural Network Control of Time Delay Teleoperation System Based on Model Approximation. <i>Sensors</i> 2021, <i>21</i>, 7443.

Sensors (Basel, Switzerland)·2026
Same author

Recent Advances in Sensor Technology for Healthcare and Biomedical Applications (Volume II).

Sensors (Basel, Switzerland)·2023
Same author

Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications.

Sensors (Basel, Switzerland)·2023
Same author

Characterization inference based on joint-optimization of multi-layer semantics and deep fusion matching network.

PeerJ. Computer science·2022
Same author

Adaptive Neural Network Control of Time Delay Teleoperation System Based on Model Approximation.

Sensors (Basel, Switzerland)·2021
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Sep 26, 2025

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

3.0K

Low-Dose CT Image Post-Processing Based on Learn-Type Sparse Transform.

Wenfeng Zheng1, Bo Yang1, Ye Xiao1

  • 1School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China.

Sensors (Basel, Switzerland)
|April 23, 2022
PubMed
Summary
This summary is machine-generated.

This study enhances low-dose X-ray Computed Tomography (CT) imaging by separating structural information from noise using learned sparse transformations. The novel approach improves image quality for better clinical disease screening and tracking.

Keywords:
image decomposition theorylow dose CTsparse representationsparse transform

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.9K
Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph
05:32

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Published on: February 21, 2025

439

Related Experiment Videos

Last Updated: Sep 26, 2025

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

3.0K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.9K
Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph
05:32

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Published on: February 21, 2025

439

Area of Science:

  • Medical Imaging
  • Image Processing
  • Computational Science

Background:

  • X-ray Computed Tomography (CT) is a valuable clinical tool for disease screening, detection, and tracking due to its clear imaging, speed, and cost-effectiveness.
  • Low-dose CT imaging is desirable to reduce patient radiation exposure but often suffers from reduced image quality, including noise and artifacts.

Purpose of the Study:

  • To improve the image quality of low-dose CT scans.
  • To develop a method that effectively separates structural information from noise and artifacts in CT images.
  • To leverage sparse representation and image decomposition for enhanced CT image reconstruction.

Main Methods:

  • The study utilizes sparse representation to learn sparse transformations of image information.
  • Image decomposition theory is combined with learned sparse transformations to separate image components.
  • Two distinct learned sparse transformations were employed: one focusing on organizational information and another on noise/artifact suppression.

Main Results:

  • The proposed method successfully separates structural information from noise and artifact information in low-dose CT images.
  • Learned sparse transformations demonstrated an improved ability to represent various image information, enhancing overall imaging effects.
  • Experimental results validated the effectiveness of the developed algorithm in improving low-dose CT image quality.

Conclusions:

  • The integration of sparse representation and image decomposition offers a promising approach for enhancing low-dose CT imaging.
  • The use of learned sparse transformations effectively mitigates noise and artifacts, leading to superior image quality.
  • This technique has the potential to improve the diagnostic accuracy and clinical utility of low-dose CT examinations.