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

4.3K
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...
4.3K
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
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

197
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...
197

You might also read

Related Articles

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

Sort by
Same author

Noncontrast Abbreviated MRI for Post-TACE Treatment Response Monitoring of Hepatocellular Carcinoma Based on Ancillary Features from LI-RADS.

Radiology·2026
Same author

LI-RADS v2018 versus KLCA-NCC v2022: comparison of probability-based HCC categories.

European radiology·2026
Same author

Bridging east and west: ancillary features and AFP to improve MRI diagnosis of ≤ 30 mm HCC.

European radiology·2026
Same author

Navigating the diagnostic 'gray zone': prospective evaluation of an integrated MRI-Biomarker model for renal allograft triage.

Annals of medicine·2026
Same author

Clinical benefits and current challenges of photon-counting detector CT in vascular imaging.

Radiology advances·2026
Same author

Evaluating Accuracy of LI-RADS Nonradiation Treatment Response Algorithm v2024 and Ancillary Features at Hepatobiliary MRI versus CT.

Radiology·2026

Related Experiment Video

Updated: Jun 6, 2025

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.4K

State-of-the-Art Deep Learning CT Reconstruction Algorithms in Abdominal Imaging.

Achille Mileto1, Lifeng Yu1, Jonathan W Revels1

  • 1From the Department of Radiology, University of Washington School of Medicine, Seattle, Wash (A.M.); Department of Radiology, Mayo Clinic, Rochester, Minn (L.Y.); Department of Radiology, New York University Grossman School of Medicine, NYU Langone Health, New York, NY (J.W.R.); Departments of Radiation Oncology (S.K.) and Abdominal Imaging (M.A.S., J.J.I.R., V.K.W., K.M.E., C.T.J.), The University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473, Houston, TX 77030-4009; Department of Radiology, Texas Children's Hospital, Houston, Tex (A.M.R.C.); and Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea (J.M.L.).

Radiographics : a Review Publication of the Radiological Society of North America, Inc
|November 29, 2024
PubMed
Summary
This summary is machine-generated.

Deep learning reconstruction (DLR) CT algorithms enhance image quality and reduce noise, especially at low radiation doses. These advanced methods offer faster reconstruction speeds while maintaining diagnostic performance in abdominal imaging.

More Related Videos

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

2.7K
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

42.5K

Related Experiment Videos

Last Updated: Jun 6, 2025

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.4K
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

2.7K
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

42.5K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Traditional CT reconstruction algorithms like FBP and IR struggle with image noise and texture preservation at low radiation doses.
  • Deep neural networks have enabled the development of deep learning reconstruction (DLR) CT algorithms.
  • DLR algorithms offer a promising solution to overcome limitations of conventional CT reconstruction methods.

Purpose of the Study:

  • To explore the technical aspects and various approaches to image synthesis in DLR CT algorithms.
  • To highlight the clinical applications of DLR algorithms in abdominal CT imaging.
  • To provide an overview of the current limitations and future outlook for DLR CT.

Main Methods:

  • Review of deep learning-based methodologies applied during or replacing traditional CT image formation.
  • Examination of DLR algorithms' impact on image noise reduction and texture preservation.
  • Analysis of reconstruction speed and diagnostic performance of DLR CT.

Main Results:

  • DLR CT algorithms effectively reduce image noise, particularly with low photon counts from reduced radiation dose protocols.
  • DLR methods preserve image texture and diagnostic performance better than FBP and IR at low radiation doses.
  • DLR algorithms demonstrate high reconstruction speed, achieving the ideal balance of image quality, low dose, and speed.

Conclusions:

  • DLR CT algorithms are effective for reducing noise and improving image quality in low-dose CT protocols.
  • Clinical evidence supports the use of DLR in abdominal imaging across various tasks.
  • DLR CT presents a significant advancement with potential for widespread clinical adoption, despite current limitations.