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...

Electron Microscope Tomography and Single-particle Reconstruction

Electron Microscope Tomography and Single-particle Reconstruction

Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...

Imaging Studies I: CT and MRI

Imaging Studies I: CT and MRI

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...

You might also read

Related Articles

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

Sort by

Same author

Improving the efficiency of normalized metal artifact reduction via a unified forward projection.

Physics in medicine and biology·2026

Same author

Dose-aware denoising diffusion model for low-dose CT.

Physics in medicine and biology·2025

Same author

Standardization of Lung CT Number Using COPD Gene2 Phantom Under Various Scanning Protocols.

Sensors (Basel, Switzerland)·2025

Same author

X-ray CT metal artifact reduction using neural attenuation field prior.

Medical physics·2025

Same author

Generalizability of convolutional neural network-based model observer in breast tomosynthesis across volume glandular fractions and signal sizes.

Medical physics·2025

Same author

Low-dose computed tomography perceptual image quality assessment.

Medical image analysis·2024

Same journal

Decomposition-based harmonization for quantitative PET imaging across scanners and radiotracers.

Medical physics·2026

Same journal

Development and evaluation of an in vivo dose-based monitoring system for electron FLASH radiation therapy.

Medical physics·2026

Same journal

A novel optical respiratory gating system with a hybrid phase-amplitude algorithm for spot-scanning proton therapy.

Medical physics·2026

Same journal

Gamma Knife treatment planning using knowledge-based reinforcement learning.

Medical physics·2026

Same journal

Development and characterization of a novel, small animal external beam irradiator using a clinical high dose rate brachytherapy source.

Medical physics·2026

Same journal

Deep learning-based dose prediction for MR-guided prostate SIB: Supporting rapid feasibility assessment and adaptive editing margin selection.

Medical physics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 9, 2025

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Continuous representation-based reconstruction for computed tomography.

Minwoo Yu¹, Junhyun Ahn², Jongduk Baek^1,3

¹Department of Artificial Intelligence, Yonsei University, Seoul, South Korea.

Medical Physics

|May 3, 2025

Summary

This summary is machine-generated.

We developed a new method called CRET for improving computed tomography (CT) image resolution. This technique enhances image quality and diagnostic capabilities without increasing data acquisition time or memory usage.

Keywords:

computed tomography (CT) image continuous image representation sinogram squeezing sinusoidal basis decoder

More Related Videos

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

Image Rendering Techniques in Postmortem Computed Tomography: Evaluation of Biological Health and Profile in Stranded Cetaceans

Published on: September 27, 2020

Related Experiment Videos

Last Updated: May 9, 2025

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

Image Rendering Techniques in Postmortem Computed Tomography: Evaluation of Biological Health and Profile in Stranded Cetaceans

Published on: September 27, 2020

Area of Science:

Medical imaging
Radiology
Computer vision

Background:

Computed tomography (CT) imaging aims for higher resolution to detect early lesions.
Limited spatial resolution of CT images restricts the full utilization of display capabilities for radiologists.

Purpose of the Study:

To enhance CT image quality using super-resolution (SR) techniques without altering data acquisition.
To overcome memory and runtime limitations of existing SR methods in CT reconstruction.

Main Methods:

Proposed a continuous image representation-based CT image reconstruction (CRET) structure.
Implemented sinogram squeezing and decoding via sinusoidal basis functions for efficient region of interest (ROI) reconstruction.
Incorporated a post-restoration step to mitigate artifacts and improve image quality.

Main Results:

CRET demonstrates superior image quality compared to other local implicit representation methods.
Post-processing further enhances CRET's image quality and performance.
Anthropomorphic observer model evaluation shows CRET outperforms conventional techniques.
CRET enables higher reconstruction resolution than training ground truth images, improving diagnostic capabilities.

Conclusions:

The CRET method effectively improves CT image resolution while managing memory and runtime constraints.
CRET offers a promising solution for enhancing diagnostic accuracy in CT imaging.
Source code for CRET is publicly available for further research and application.