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

268
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...
268
Computed Tomography01:10

Computed Tomography

7.9K
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...
7.9K
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

764
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...
764

You might also read

Related Articles

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

Sort by
Same author

Request and reporting models for computed tomography in the multidisciplinary management of cancer patients: consensus between the Italian Society of Medical and Interventional Radiology (SIRM) and the Italian Society of Medical Oncology (AIOM).

La Radiologia medica·2026
Same author

Reduced environmental impact in body CT imaging with deep learning reconstruction: experience of a high-volume tertiary referral center.

Insights into imaging·2026
Same author

Body composition as a predictor of cancer-related death in colon cancer: an AI-based volumetric analysis.

La Radiologia medica·2026
Same author

A PSC-tailored deep learning model for liver segmentation on fat-saturated T2-weighted MR: Robustness to hepatic dysmorphia and multicentre generalisability.

European journal of radiology·2026
Same author

Ultrasound elastography in pediatric care: bridging innovation and noninvasive diagnostics.

Italian journal of pediatrics·2026
Same author

Virtual non-contrast images from dual-layer spectral CT: comparison with true non-contrast across abdominal structures.

European radiology·2026

Related Experiment Video

Updated: Jan 10, 2026

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

1.6K

Improved image quality and dose reduction in liver CT using deep learning-based reconstruction: A comparative study.

Cesare Maino1, Paolo Niccolò Franco1, Elzebieta Szafranska2

  • 1Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, MB, Italy.

European Journal of Radiology
|November 20, 2025
PubMed
Summary

Deep Learning-based Image Reconstruction (DLIR) significantly improves liver CT image quality and reduces radiation dose compared to Hybrid Iterative Reconstruction (HIR). This advanced DLIR technique offers better lesion visualization and patient safety in hepatic imaging.

Keywords:
AlgorithmsArtificial intelligenceComputer-assistedDeep learningDiagnostic imagingImage processingLiverRadiation dosageTomographyX-ray computed

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

3.3K
Author Spotlight: Enhancing Transplantation Research Through MicroCT Angiography in Murine Models
09:23

Author Spotlight: Enhancing Transplantation Research Through MicroCT Angiography in Murine Models

Published on: September 22, 2023

3.6K

Related Experiment Videos

Last Updated: Jan 10, 2026

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

1.6K
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

3.3K
Author Spotlight: Enhancing Transplantation Research Through MicroCT Angiography in Murine Models
09:23

Author Spotlight: Enhancing Transplantation Research Through MicroCT Angiography in Murine Models

Published on: September 22, 2023

3.6K

Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Computed Tomography (CT) is crucial for diagnosing focal liver lesions.
  • Traditional image reconstruction methods like Hybrid Iterative Reconstruction (HIR) have limitations in image quality and radiation dose.
  • Deep Learning-based Image Reconstruction (DLIR) offers a potential advancement in CT image processing.

Purpose of the Study:

  • To compare the image quality and radiation dose of CT scans reconstructed using DLIR versus HIR in patients with focal liver lesions.
  • To evaluate the quantitative and qualitative performance of DLIR in hepatic CT imaging.

Main Methods:

  • 153 patients with focal liver lesions underwent two CT scans using scanners with DLIR and HIR algorithms.
  • Image quality was assessed using a Likert scale, with measurements of CT attenuation, noise (SD), SNR, and CNR.
  • Radiation dose was quantified using CTDI and DLP values.

Main Results:

  • DLIR demonstrated significantly higher image quality scores compared to HIR (p < 0.001).
  • DLIR yielded higher CT attenuation, lower noise (SD), and improved SNR and CNR across all regions (p < 0.001).
  • Radiation dose was significantly lower with DLIR compared to HIR (p < 0.001).

Conclusions:

  • DLIR significantly enhances both qualitative and quantitative image quality in liver CT.
  • DLIR achieves a substantial reduction in radiation dose for patients undergoing hepatic CT.
  • DLIR represents a superior reconstruction algorithm for focal liver lesion detection and patient safety.