Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Computed Tomography

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...

You might also read

Related Articles

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

Sort by

Same author

Vessel Wall Imaging in 1.5 T MRI Using Deep Learning Reconstruction: Prospective Evaluation of Interchangeability With Standard 3 T MRI.

Investigative radiology·2026

Same author

Accelerated Deep-Learning-Based Image Reconstruction for 3D T2 Dark-Fluid in Imaging of Multiple Sclerosis.

Investigative radiology·2026

Same author

Hearing loss and executive functions - results from a population-based cohort study.

PloS one·2026

Same author

Correction: Association of sarcopenia with survival and treatment response in brain metastasis of non-small cell lung cancer.

Scientific reports·2026

Same author

Impact of Deep-Learning Reconstruction on MRI Workflows: A Retrospective Analysis at a Large Academic Tertiary Center.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin·2026

Same author

Clinicopsychological stratification of recovery outcomes in cerebral aneurysm patients following cluster analysis.

PNAS nexus·2026

Search research articles

Related Experiment Video

Updated: May 25, 2025

A Sectioning, Coring, and Image Processing Guide for High-Throughput Cortical Bone Sample Procurement and Analysis for Synchrotron Micro-CT

A Sectioning, Coring, and Image Processing Guide for High-Throughput Cortical Bone Sample Procurement and Analysis for Synchrotron Micro-CT

Published on: June 12, 2020

Deep Learning-Enhanced Ultra-high-resolution CT Imaging for Superior Temporal Bone Visualization.

Lavinia Brockstedt¹, Nils F Grauhan¹, Andrea Kronfeld¹

¹Department of Neuroradiology, University Medical Centre Mainz, Johannes Gutenberg University Mainz, Mainz, Germany (L.B., N.F.G., A.K., M.A.A.M., A.S., M.A.B., A.E.O.).

Academic Radiology

|February 25, 2025

Summary

This summary is machine-generated.

Deep learning-based reconstruction (DLR) significantly enhances ultra-high-resolution CT scans of the temporal bone. This advanced imaging technique improves image quality and diagnostic performance in both adults and children.

Keywords:

Computed tomography Deep learning Image quality Temporal bone Ultra-high resolution

More Related Videos

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

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

Published on: June 21, 2011

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

Related Experiment Videos

Last Updated: May 25, 2025

A Sectioning, Coring, and Image Processing Guide for High-Throughput Cortical Bone Sample Procurement and Analysis for Synchrotron Micro-CT

A Sectioning, Coring, and Image Processing Guide for High-Throughput Cortical Bone Sample Procurement and Analysis for Synchrotron Micro-CT

Published on: June 12, 2020

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

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

Published on: June 21, 2011

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

Area of Science:

Radiology
Medical Imaging
Computational Imaging

Background:

Ultra-high-resolution computed tomography (UHR-CT) is crucial for detailed temporal bone imaging.
Assessing image quality improvements in pediatric and adult temporal bone CT is essential for diagnosis.

Purpose of the Study:

To evaluate the image quality of temporal bone UHR-CT using hybrid iterative reconstruction (HIR) and a novel deep learning-based reconstruction (DLR) algorithm (AiCE Inner Ear).
To compare the diagnostic performance of DLR-enhanced UHR-CT against traditional HIR methods in adults and children.

Main Methods:

A retrospective study included 57 temporal bones from 35 patients (adults and children).
Images were reconstructed using HIR (normal and UHR) and DLR (AiCE Inner Ear) at UHR.
Radiologists assessed 18 anatomical structures using a 5-point Likert scale; signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured.

Main Results:

DLR-enhanced UHR-CT significantly improved subjective image quality, reduced noise, and increased SNR and CNR in both adult and pediatric protocols (p<0.024).
DLR notably enhanced the visualization of critical structures like the stapedius muscle tendon, tympanic membrane, and osseous spiral lamina.

Conclusions:

Vendor-specific DLR significantly enhances temporal bone UHR-CT image quality.
This advanced reconstruction technique improves diagnostic performance for temporal bone imaging in pediatric and adult populations.