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...
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.

You might also read

Related Articles

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

Sort by
Same author

Follow-up of periapical inflammation after root canal retreatment and surgical endodontic treatment using dental-dedicated magnetic resonance imaging: a case report.

Oral radiology·2026
Same author

A Protocol for the Development of a Three-Dimensional Classification of Endodontic Lesions Scheduled for Surgery.

International endodontic journal·2026
Same author

Do Artifacts From Dental Implants Impair the Diagnosis of Simulated Internal Root Resorption in Cone-Beam CT?

Clinical and experimental dental research·2026
Same author

Extended Reality in Endodontics: A Review of Current Applications and Future Potential.

International endodontic journal·2026
Same author

What Drives Metal Artifacts in CBCT? A Comparative Study of Detector Types and Metallic Object Configurations.

Radiology research and practice·2026
Same author

Reply to: Comment on "Association between tooth agenesis and root morphology assessed by periapical radiographs".

Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie·2026

Related Experiment Video

Updated: Jul 9, 2026

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
05:49

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Deep learning-driven super-resolution for cone-beam computed tomography: An ex vivo proof-of-concept study using

Hossein Mohammad-Rahimi1,2, Konstantinos Verdelis3, Rubens Spin-Neto1

  • 1Department of Dentistry and Oral Health, Aarhus University, Aarhus, Denmark.

Imaging Science in Dentistry
|July 8, 2026
PubMed
Summary

Deep learning super-resolution models enhance simulated cone-beam computed tomography (CBCT) images. The local texture estimator (LTE) model showed superior sharpness and improved crack visibility in dental imaging.

Keywords:
Artificial IntelligenceCone-Beam Computed TomographyDeep LearningImage Enhancement

More Related Videos

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

High Resolution 3D Imaging of Ex-Vivo Biological Samples by Micro CT
08:57

High Resolution 3D Imaging of Ex-Vivo Biological Samples by Micro CT

Published on: June 21, 2011

Related Experiment Videos

Last Updated: Jul 9, 2026

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
05:49

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

High Resolution 3D Imaging of Ex-Vivo Biological Samples by Micro CT
08:57

High Resolution 3D Imaging of Ex-Vivo Biological Samples by Micro CT

Published on: June 21, 2011

Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Cone-beam computed tomography (CBCT) is crucial in dental diagnostics.
  • Simulated CBCT images often lack sufficient resolution and clarity for detailed analysis.
  • Deep learning (DL) offers potential for image enhancement.

Purpose of the Study:

  • To develop and compare DL-based super-resolution (SR) models for enhancing simulated CBCT images.
  • To evaluate the effectiveness of different DL architectures against traditional interpolation methods.
  • To assess the impact of SR on diagnostic features like crack visibility.

Main Methods:

  • Micro-computed tomography data from 51 teeth were used to create simulated CBCT images.
  • Three DL models (SRCNN, LTE, SwinIR) and bicubic interpolation were tested.
  • Objective image quality metrics (PSNR, SSIM, LPIPS, DISTS) and subjective evaluations by dentists and observers were employed.

Main Results:

  • All DL models significantly outperformed bicubic interpolation in objective image quality assessments.
  • The Local Texture Estimator (LTE) model demonstrated the best perceptual sharpness and significantly improved crack visibility.
  • LTE showed superior performance in learned perceptual image patch similarity (LPIPS) and deep image structure and texture similarity (DISTS).

Conclusions:

  • Deep learning super-resolution models effectively enhance simulated CBCT images.
  • The LTE model shows particular promise for improving diagnostic accuracy in dentistry due to enhanced sharpness and crack detection.
  • Further research can explore DL-based SR for real-world CBCT applications.