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

284
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...
284
Computed Tomography01:10

Computed Tomography

8.0K
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...
8.0K

You might also read

Related Articles

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

Sort by
Same author

Single-sequence low-field 3D T1-weighted MRI for scaphoid fracture detection compared to radiography: prospective diagnostic accuracy pilot study.

Emergency radiology·2026
Same author

Evaluation of Projection Images for Visual Quality Control of Automated Left and Right Lung Segmentations on T1-Weighted MRI in Large-Scale Clinical Cohort Studies.

Tomography (Ann Arbor, Mich.)·2025
Same author

AI-Based 3D-Segmentation Quantifies Sarcopenia in Multiple Myeloma Patients.

Diagnostics (Basel, Switzerland)·2025
Same author

Magnesium Depletion, Metabolic Impairment, and Cardiac Alterations: The NAKO-MRI Study With Mendelian Randomization.

The Journal of clinical endocrinology and metabolism·2025
Same author

Fully Automated Assessment of Cardiac Chamber Volumes and Myocardial Mass on Non-Contrast Chest CT with a Deep Learning Model: Validation Against Cardiac MR.

Diagnostics (Basel, Switzerland)·2025
Same author

[Photon-Counting Detector CT: Advances and Clinical Applications in Cardiovascular Imaging].

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin·2024

Related Experiment Video

Updated: Jan 16, 2026

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

1.1K

Performance of a Deep Learning Reconstruction Method on Clinical Chest-Abdomen-Pelvis Scans from a Dual-Layer

Christopher Schuppert1,2,3, Stefanie Rahn2,3, Nikolas D Schnellbächer4

  • 1Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany.

Tomography (Ann Arbor, Mich.)
|September 26, 2025
PubMed
Summary

Deep learning reconstruction (DLR) offers improved soft tissue CT image quality. Smoother DLR settings reduced image noise and enhanced overall perception compared to filtered back projection and iterative model reconstruction.

Keywords:
computed tomographydeep learning reconstructiondenoisingimage noiseimage quality

More Related Videos

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

2.0K
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

Related Experiment Videos

Last Updated: Jan 16, 2026

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

1.1K
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

2.0K
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

Area of Science:

  • Medical Imaging
  • Radiology
  • Artificial Intelligence in Healthcare

Background:

  • Computed tomography (CT) image reconstruction is crucial for diagnostic accuracy.
  • Established methods like filtered back projection (FBP) and iterative model reconstruction (IMR) have limitations in soft tissue visualization.
  • Deep learning reconstruction (DLR) presents a novel approach to enhance CT image quality.

Purpose of the Study:

  • To evaluate the performance and robustness of DLR for soft tissue CT reconstruction.
  • To compare DLR against FBP and IMR in terms of image noise and attenuation stability.
  • To assess reader perception of image quality between DLR and IMR.

Main Methods:

  • Chest-abdomen-pelvis CT scans (n=99) were reconstructed using FBP, IMR, and DLR ('standard', 'sharper', 'smoother').
  • Quantitative assessment involved attenuation stability and image noise in ten structures.
  • Qualitative assessment used a Likert scale for overall image quality perception.

Main Results:

  • DLR significantly reduced image noise compared to FBP across all settings.
  • 'Smoother' DLR demonstrated lower or equivalent noise levels compared to IMR.
  • Experienced readers rated 'smoother' DLR images significantly higher for overall quality than IMR images (3.7 vs. 2.3).

Conclusions:

  • The 'smoother' DLR setting provides superior soft tissue CT image quality compared to FBP and IMR.
  • DLR achieves this improvement with objectively lower or equivalent noise levels.
  • DLR represents a promising advancement in CT image reconstruction technology.