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

Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

323
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...
323
Computed Tomography01:10

Computed Tomography

8.1K
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...
8.1K
Oxidation-Reduction Reactions03:11

Oxidation-Reduction Reactions

75.3K
Oxidation–Reduction Reactions
75.3K
Metal-Ligand Bonds02:51

Metal-Ligand Bonds

24.1K
The hemoglobin in the blood, the chlorophyll in green plants, vitamin B-12, and the catalyst used in the manufacture of polyethylene all contain coordination compounds. Ions of the metals, especially the transition metals, are likely to form complexes.
In these complexes, transition metals form coordinate covalent bonds, a kind of Lewis acid-base interaction in which both of the electrons in the bond are contributed by a donor (Lewis base) to an electron acceptor (Lewis acid). The Lewis acid in...
24.1K
Bonding in Metals02:32

Bonding in Metals

52.2K
Metallic bonds are formed between two metal atoms. A simplified model to describe metallic bonding has been developed by Paul Drüde called the “Electron Sea Model”. 
52.2K
Alkali Metals03:06

Alkali Metals

24.3K
Group 1 elements are soft and shiny metallic solids. They are malleable, ductile, and good conductors of heat and electricity. The melting points of the alkali metals are unusually low for metals and decrease going down the group, while the density increases going down the group with the exception of potassium (Table 1).
Table 1: Properties of the alkali metals
24.3K

You might also read

Related Articles

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

Sort by
Same author

Assessing transcriptomic heterogeneity of single-cell RNASeq data by bulk-level gene expression data.

BMC bioinformatics·2024
Same author

Meta-analysis of the effectiveness of heparin in suppressing physiological myocardial FDG uptake in PET/CT.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology·2023
Same author

ASSESSING BODY DOSE RATE CONSTANT AND EFFECTIVE BODY ABSORPTION FACTOR IN TAIWANESE REFERENCE PHANTOMS.

Radiation protection dosimetry·2023
Same author

Diagnosis of Osteoporosis by Quantifying Volumetric Bone Mineral Density of Lumbar Vertebrae Using Abdominal CT Images and Two-Compartment Model.

Healthcare (Basel, Switzerland)·2023
Same author

Effective suppression of myocardial glucose uptake using predesigned low-carbohydrate boxed meals.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology·2022
Same author

Using Cine-Averaged CT With the Shallow Breathing Pattern to Reduce Respiration-Induced Artifacts for Thoracic Cavity PET/CT Scans.

AJR. American journal of roentgenology·2019
Same journal

Bayesian Convolutional Neural Networks in Medical Imaging Classification: A Promising Solution for Deep Learning Limits in Data Scarcity Scenarios.

Journal of digital imaging·2023
Same journal

Detecting and Characterizing Inferior Vena Cava Filters on Abdominal Computed Tomography with Data-Driven Computational Frameworks.

Journal of digital imaging·2023
Same journal

DMCA-GAN: Dual Multilevel Constrained Attention GAN for MRI-Based Hippocampus Segmentation.

Journal of digital imaging·2023
Same journal

Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms.

Journal of digital imaging·2023
Same journal

Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review.

Journal of digital imaging·2023
Same journal

External Validation of Robust Radiomic Signature to Predict 2-Year Overall Survival in Non-Small-Cell Lung Cancer.

Journal of digital imaging·2023
See all related articles

Related Experiment Video

Updated: Jan 26, 2026

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

3.4K

Model Image-Based Metal Artifact Reduction for Computed Tomography.

Dmytro Luzhbin1, Jay Wu2

  • 1Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, No. 155, Sec. 2, Linong Street, Taipei, Taiwan, 11221, Republic of China.

Journal of Digital Imaging
|April 24, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel post-processing algorithm to eliminate metal artifacts in computed tomography (CT) images. The method enhances diagnostic value by improving image quality for clinical applications.

Keywords:
Computed tomographyImage inpaintingMetal artifact reductionVirtual sinogram

More Related Videos

Tissue Preparation Techniques for Contrast-Enhanced Micro Computed Tomography Imaging of Large Mammalian Cardiac Models with Chronic Disease
12:15

Tissue Preparation Techniques for Contrast-Enhanced Micro Computed Tomography Imaging of Large Mammalian Cardiac Models with Chronic Disease

Published on: February 8, 2022

2.9K
Combined Near-infrared Fluorescent Imaging and Micro-computed Tomography for Directly Visualizing Cerebral Thromboemboli
13:10

Combined Near-infrared Fluorescent Imaging and Micro-computed Tomography for Directly Visualizing Cerebral Thromboemboli

Published on: September 25, 2016

10.3K

Related Experiment Videos

Last Updated: Jan 26, 2026

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

3.4K
Tissue Preparation Techniques for Contrast-Enhanced Micro Computed Tomography Imaging of Large Mammalian Cardiac Models with Chronic Disease
12:15

Tissue Preparation Techniques for Contrast-Enhanced Micro Computed Tomography Imaging of Large Mammalian Cardiac Models with Chronic Disease

Published on: February 8, 2022

2.9K
Combined Near-infrared Fluorescent Imaging and Micro-computed Tomography for Directly Visualizing Cerebral Thromboemboli
13:10

Combined Near-infrared Fluorescent Imaging and Micro-computed Tomography for Directly Visualizing Cerebral Thromboemboli

Published on: September 25, 2016

10.3K

Area of Science:

  • Medical Imaging
  • Image Processing
  • Computational Science

Background:

  • Metal implants in patients cause severe artifacts in computed tomography (CT) images.
  • These artifacts degrade image quality, leading to loss of diagnostic information and rendering images unusable.

Purpose of the Study:

  • To develop and evaluate a post-processing algorithm for metal artifact reduction (MAR) in CT images.
  • To enhance the diagnostic utility of CT images affected by metal artifacts.

Main Methods:

  • A novel MAR algorithm utilizing thresholding and k-means clustering with spatial information for tissue-class segmentation.
  • Integration of an image inpainting technique to improve segmentation accuracy in severely corrupted CT images.
  • Algorithm validation using a water phantom and clinical CT datasets.

Main Results:

  • The proposed method effectively eliminates metal artifacts and restores accurate CT numbers for various tissues.
  • Preservation of crucial edge and contrast information, enabling accurate tissue attenuation map reconstruction.
  • Artifact-corrected images are suitable for subsequent clinical applications like 3D rendering and radiotherapy dose estimation.

Conclusions:

  • The developed post-processing algorithm successfully reduces metal artifacts in CT images.
  • The enhanced image quality supports a wider range of clinical applications without relying on raw sinogram data.
  • This technique offers a valuable tool for improving diagnostic accuracy in patients with metal implants.