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

9.5K
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...
9.5K
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

1.6K
Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
1.6K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

677
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...
677
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

567
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...
567

You might also read

Related Articles

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

Sort by
Same author

Risk elements contamination in the riverbed sediments of the Xiangjiang River, China: a review.

Environmental monitoring and assessment·2026
Same author

Regional Biomechanical and Topographic Changes after Transepithelial vs. Epithelium-off Continuous Accelerated Corneal Cross-linking in Keratoconus: Updated Stress-Strain Index as a Superior Biomarker.

Ophthalmology science·2026
Same author

Preventive effects and underlying mechanisms of Ilex rotunda Thunb.-Cyperus rotundus L. herb pair extract on avian colibacillosis in chickens.

Poultry science·2026
Same author

A radio-pathological fusion model for predicting PD-L1 expression and immunotherapy response in non-small cell lung cancer.

Insights into imaging·2026
Same author

Spherical radiomics for radiogenomic assessment of glioblastoma heterogeneity.

Neuro-oncology advances·2026
Same author

Multicenter development and external validation of clinical-radiomics models to predict surgically confirmed upstaging in biopsy-proven DCIS using DCE-MRI.

Journal of applied clinical medical physics·2026
Same journal

Deep learning-based dose prediction to enhance planning efficiency in cervical brachytherapy with hybrid applicators.

Physics in medicine and biology·2026
Same journal

Corrigendum: Referenceless MR thermometry-a comparison of five methods (2017<i>Phys. Med. Biol</i>.<b>62</b>1-16).

Physics in medicine and biology·2026
Same journal

Corrigendum: Measured and Monte Carlo simulated electron backscatter to the monitor chamber for the varian TrueBeam linac (2016<i>Phys. Med. Biol</i>.<b>61</b>8779).

Physics in medicine and biology·2026
Same journal

Corrigendum: 3D range-modulator for scanned particle therapy: development, Monte Carlo simulations and experimental evaluation (2017<i>Phys. Med. Biol</i>.<b>62</b>7075).

Physics in medicine and biology·2026
Same journal

Recent progress in applications of computing to radiotherapy (ICCR 2016).

Physics in medicine and biology·2026
Same journal

Novel TMS coils designed using an inverse boundary element method.

Physics in medicine and biology·2026
See all related articles

Related Experiment Video

Updated: Mar 31, 2026

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

43.8K

Iterative CT shading correction with no prior information.

Pengwei Wu1, Xiaonan Sun, Hongjie Hu

  • 1Sir Run Run Shaw Hospital, Zhejiang University School of Medicine; Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, 310016, People's Republic of China.

Physics in Medicine and Biology
|October 15, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to correct shading artifacts in CT images without prior data. The technique improves image uniformity and accuracy, making CT scans more reliable for diagnosis.

More Related Videos

Author Spotlight: Enhanced Quantification of Cardiovascular Calcification Progression for Longitudinal Micro PET/CT Studies in Small Research Animals
08:02

Author Spotlight: Enhanced Quantification of Cardiovascular Calcification Progression for Longitudinal Micro PET/CT Studies in Small Research Animals

Published on: November 15, 2024

1.1K
Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.8K

Related Experiment Videos

Last Updated: Mar 31, 2026

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

43.8K
Author Spotlight: Enhanced Quantification of Cardiovascular Calcification Progression for Longitudinal Micro PET/CT Studies in Small Research Animals
08:02

Author Spotlight: Enhanced Quantification of Cardiovascular Calcification Progression for Longitudinal Micro PET/CT Studies in Small Research Animals

Published on: November 15, 2024

1.1K
Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.8K

Area of Science:

  • Medical Imaging
  • Image Processing
  • Computational Imaging

Background:

  • Shading artifacts in CT images degrade image quality due to scatter contamination and beam-hardening effects.
  • These artifacts can obscure details and lead to misdiagnosis, necessitating effective correction methods.

Purpose of the Study:

  • To develop a novel, generalizable framework for correcting low-frequency shading artifacts in CT images.
  • To eliminate reliance on prior information or specific imaging conditions for artifact correction.

Main Methods:

  • Image segmentation to create a tissue-specific template, followed by residual error generation.
  • Forward projection of residual image and low-pass filtering to estimate shading errors.
  • Iterative compensation map reconstruction using filtered error and FDK algorithm for correction.

Main Results:

  • Reduced overall CT number error from over 200 HU to less than 30 HU.
  • Improved spatial uniformity by a factor of 1.5.
  • Preservation of low-contrast objects after correction.

Conclusions:

  • An effective iterative algorithm for CT shading correction is proposed.
  • The method utilizes general anatomical information without requiring prior knowledge.
  • This approach offers a practical and attractive general solution for CT shading correction.