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

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...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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...

You might also read

Related Articles

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

Sort by
Same author

Abdominopelvic computed tomography during pregnancy and the risk of congenital malformations: protocol for a nationwide population-based cohort study in South Korea.

BMJ open·2026
Same author

Improving prediction of ypT0-1N0 response in rectal cancer: the added value of gross tumor type to magnetic resonance tumor regression grade after chemoradiotherapy in a retrospective cohort study.

Annals of surgical treatment and research·2026
Same author

Comparison of Clinical Performance Between Digital Breast Tomosynthesis and MammouS-N.

Tomography (Ann Arbor, Mich.)·2026
Same author

Effectiveness of AI-CAD Software for Breast Cancer Detection in Automated Breast Ultrasound.

Journal of imaging informatics in medicine·2025
Same author

Appendicitis.

Nature reviews. Disease primers·2025
Same author

Clinicopathological Factors Influencing PD-L1 Expression and The Effect of Immune Checkpoint Inhibitors on Survival Outcomes in Patients with Gastric Cancer Depending on Sex in a Tertiary Hospital in South Korea.

Cancer research and treatment·2025
Same journal

The Banality of Cancer: Entropy As a Third Pillar of Lung Nodule Risk Assessment.

AJR. American journal of roentgenology·2026
Same journal

A Narrow Window for Artificial Intelligence-Generated Synthetic Temporal Bone CT From MRI.

AJR. American journal of roentgenology·2026
Same journal

From Uncertainty to Actionable Management: The Isolated Abnormal Axillary Lymph Node.

AJR. American journal of roentgenology·2026
Same journal

Beyond Detection: Translating Artificial Intelligence-Driven Opportunistic Screening Into Clinical Action.

AJR. American journal of roentgenology·2026
Same journal

Navigating PSMA PET Radiopharmaceuticals: Clinical and Operational Factors.

AJR. American journal of roentgenology·2026
Same journal

From Mesenteric Ischemia to Intestinal Stroke.

AJR. American journal of roentgenology·2026
See all related articles

Related Experiment Video

Updated: Jul 3, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Differences in compression artifacts on thin- and thick-section lung CT images.

Vasundhara Bajpai1, Kyoung Ho Lee, Bohyoung Kim

  • 1Department of Radiology, Seoul National University Bundang Hospital, 300 Gumi-dong, Bundang-gu, Seongnam-si, Gyeonggi-do, 463-707, Korea.

AJR. American Journal of Roentgenology
|July 24, 2008
PubMed
Summary
This summary is machine-generated.

Thin-section lung CT images show more Joint Photographic Experts Group (JPEG) 2000 compression artifacts than thick-section images. Adjusting compression levels for lung CT requires considering section thickness to maintain image quality.

More Related Videos

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Point-of-Care Lung Ultrasound in Adults: Image Acquisition
09:17

Point-of-Care Lung Ultrasound in Adults: Image Acquisition

Published on: March 3, 2023

Related Experiment Videos

Last Updated: Jul 3, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Point-of-Care Lung Ultrasound in Adults: Image Acquisition
09:17

Point-of-Care Lung Ultrasound in Adults: Image Acquisition

Published on: March 3, 2023

Area of Science:

  • Medical Imaging
  • Image Compression
  • Radiology

Background:

  • Joint Photographic Experts Group (JPEG) 2000 is a wavelet-based image compression standard.
  • CT image compression is crucial for storage and transmission, but can introduce artifacts.
  • Thin-section CT provides higher resolution but may be more susceptible to compression artifacts.

Purpose of the Study:

  • To compare Joint Photographic Experts Group (JPEG) 2000 compression artifacts in thin-section versus thick-section lung CT images.
  • To evaluate the impact of different compression ratios on image quality for varying section thicknesses.

Main Methods:

  • 35 thin-section (1 mm) and 35 thick-section (5 mm) lung CT images were compressed using JPEG 2000 at ratios from 4:1 to 15:1.
  • Pixels outside the lung were replaced with original values to isolate artifact analysis.
  • Three radiologists independently graded compression artifacts, and peak signal-to-noise ratio (PSNR) was calculated.

Main Results:

  • Thin sections exhibited significantly higher compression artifact grades (p < 0.009) at 10:1 and 15:1 compression ratios compared to thick sections.
  • Peak signal-to-noise ratio (PSNR) was significantly lower for thin sections (p < 0.0001).
  • The percentage of distinguishable image pairs (artifacts present) was significantly higher for thin sections at 10:1 compression (p < 0.006).

Conclusions:

  • Lung CT images demonstrate greater susceptibility to Joint Photographic Experts Group (JPEG) 2000 compression artifacts with thinner sections.
  • Section thickness is a critical factor influencing artifact visibility and should be considered when applying compression to lung CT data.