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 III: Computed Tomography

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...

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

Biomimetic characterization by micro-computed tomography (μCT) of 3D hollow fibre membrane network bioreactors for tissue engineering.

Biomaterials science·2026

Same author

Method for auto-alignment and determination of parameter space in dual-phase grating interferometry.

Optics express·2024

Same author

Pixel-wise beam-hardening correction for dark-field signal in X-ray dual-phase grating interferometry.

Optics express·2023

Same author

RORγt inhibition ameliorates IL-23 driven experimental psoriatic arthritis by predominantly modulating γδ-T cells.

Rheumatology (Oxford, England)·2023

Same author

A20 controls RANK-dependent osteoclast formation and bone physiology.

EMBO reports·2022

Same author

Detecting thin adhesive coatings in wood fiber materials with laboratory-based dual-energy computed tomography (DECT).

Scientific reports·2022

Search research articles

Related Experiment Video

Updated: Jun 14, 2026

Tree Core Analysis with X-ray Computed Tomography

Tree Core Analysis with X-ray Computed Tomography

Published on: September 22, 2023

A multiresolution approach to iterative reconstruction algorithms in X-ray computed tomography.

Yoni De Witte¹, Jelle Vlassenbroeck, Luc Van Hoorebeke

¹Centre for X-Ray Tomography, Department of Physics and Astronomy, Ghent University, Ghent, Belgium. Yoni.DeWitte@UGent.be

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|March 31, 2010

Summary

This summary is machine-generated.

Iterative reconstruction in computed tomography is computationally intensive. A new multiresolution method significantly reduces memory needs, enabling faster, large-volume reconstructions with limited resources.

More Related Videos

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

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

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

Published on: June 21, 2011

Related Experiment Videos

Last Updated: Jun 14, 2026

Tree Core Analysis with X-ray Computed Tomography

Tree Core Analysis with X-ray Computed Tomography

Published on: September 22, 2023

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

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

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

Published on: June 21, 2011

Area of Science:

Medical Imaging
Computational Imaging
Image Reconstruction

Background:

Iterative reconstruction methods in computed tomography (CT) offer superior image quality but face computational challenges.
High-resolution CT and large datasets (giga voxels) exacerbate computational demands and memory requirements.
Current limitations hinder the widespread adoption of advanced iterative algorithms in practical CT applications.

Purpose of the Study:

To address the computational and memory limitations of iterative reconstruction algorithms in CT.
To introduce a novel multiresolution reconstruction approach for large-volume CT data.
To enable the practical application of iterative reconstruction in high-resolution CT using limited resources.

Main Methods:

Development of a novel multiresolution reconstruction algorithm.
Implementation of the algorithm on a graphical processing unit (GPU) for efficient parallel processing.
Evaluation of memory reduction and image quality preservation for large-scale CT datasets.

Main Results:

The multiresolution approach significantly reduces memory requirements for large-volume CT reconstruction.
Reconstructed image quality is maintained with minimal impact from the multiresolution technique.
The GPU-accelerated multiresolution method achieves acceptable reconstruction speeds on limited hardware.

Conclusions:

The proposed multiresolution reconstruction method effectively overcomes memory and computational barriers in CT.
This approach facilitates the practical use of iterative algorithms for high-resolution, large-volume CT imaging.
It enables efficient and accessible advanced image reconstruction with reduced resource demands.