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 for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

3
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...
3
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

215
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...
215

You might also read

Related Articles

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

Sort by
Same author

Artificial Intelligence (Pattern Recognition) in Musculoskeletal Imaging: The Future or Hype?

Seminars in musculoskeletal radiology·2026
Same author

Development of an Algorithm to Estimate Fat-Free Mass to Optimize Contrast Injection for Computed Tomography Imaging of the Liver.

Journal of computer assisted tomography·2026
Same author

Overall risk of cancer incidence attributable to adult body CT examinations: impact of a seven-year continuous quality improvement program.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)·2026
Same author

Economic evaluations of AI applications in radiology: a systematic review.

European radiology·2026
Same author

Impact of AI assistance on radiologist interpretation of knee MRI.

European radiology·2025
Same author

Optimizing patient and staff radiation exposure in interventional cardiology: how to achieve it.

Radiation protection dosimetry·2025

Related Experiment Video

Updated: Jun 11, 2025

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease
08:41

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

Published on: March 24, 2023

1.1K

Validation of a multi-parameter algorithm for personalized contrast injection protocol in liver CT.

Hugues G Brat1, Benoit Dufour1, Natalie Heracleous1

  • 1Institut de Radiologie de Sion, Groupe 3R, Sion, Switzerland.

European Radiology Experimental
|October 9, 2024
PubMed
Summary

A new algorithm personalizes contrast injection for liver computed tomography (CT) scans, ensuring consistent imaging quality. This approach enhances diagnostic accuracy for liver conditions and improves patient management.

Keywords:
AbdomenBody compositionContrast mediaLiverMultidetector computed tomography

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
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

1.1K

Related Experiment Videos

Last Updated: Jun 11, 2025

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease
08:41

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

Published on: March 24, 2023

1.1K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
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

1.1K

Area of Science:

  • Radiology
  • Medical Imaging
  • Personalized Medicine

Background:

  • Optimizing contrast injection in liver computed tomography (CT) is crucial for image quality and patient safety.
  • Personalized contrast delivery aims for reproducible liver enhancement around 50 Hounsfield Units (HU).

Purpose of the Study:

  • To evaluate a novel algorithm for personalized contrast injection in liver CT.
  • To achieve consistent liver enhancement centered at 50 HU.

Main Methods:

  • Prospective, multicenter data collection of adult abdominal CT scans from September 2020 to August 2022.
  • Utilized a web interface integrating radiology information system data and radiographer-inputted patient specifics (e.g., fat-free mass).
  • Algorithm calculated contrast volume and injection rate, with kVp adjusted based on patient habitus (80, 100, or 120 kVp).

Main Results:

  • 384 adult patients were enrolled.
  • 72.1% of examinations achieved the target liver enhancement range of 40-60 HU.
  • Iodine dose varied significantly with kVp and patient sex but not contrast agent type.

Conclusions:

  • Validated a novel algorithm for personalized contrast injection in adult abdominal CT.
  • The algorithm consistently achieves liver enhancement centered at 50 HU.
  • Offers potential for improved diagnostic accuracy and patient management, especially for liver malignancies.