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...

You might also read

Related Articles

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

Sort by

Same author

Immunosuppressive therapies adversely affect blood biochemical parameters in patients with inflammatory bowel disease: a meta-analysis.

The Journal of international medical research·2019

Same author

Facile synthesis of Ag-CuO/SBA-15 for aerobic epoxidation of olefins with high activity.

Nanotechnology·2019

Same author

Reinforcement of Polylactic Acid for Fused Deposition Modeling Process with Nano Particles Treated Bamboo Powder.

Polymers·2019

Same author

Comparison of deep learning and human observer performance for detection and characterization of simulated lesions.

Journal of medical imaging (Bellingham, Wash.)·2019

Same author

Bending Flexibility of Moso Bamboo (<i>Phyllostachys Edulis</i>) with Functionally Graded Structure.

Materials (Basel, Switzerland)·2019

Same author

CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE).

IEEE transactions on medical imaging·2019

Same journal

Battle royale optimizer for multilevel image thresholding.

The Journal of supercomputing·2025

Same journal

MOBRO: multi-objective battle royale optimizer.

The Journal of supercomputing·2025

Same journal

Optimizing inference of segmentation on high-resolution images in MLExchange.

The Journal of supercomputing·2025

Same journal

Topic sentiment analysis based on deep neural network using document embedding technique.

The Journal of supercomputing·2023

Same journal

AEGA: enhanced feature selection based on ANOVA and extended genetic algorithm for online customer review analysis.

The Journal of supercomputing·2023

Same journal

A Fechner multiscale local descriptor for face recognition.

The Journal of supercomputing·2023

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 11, 2026

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

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

Published on: November 23, 2019

Parallelism of iterative CT reconstruction based on local reconstruction algorithm.

Junjun Deng¹, Hengyong Yu, Jun Ni

¹Department of Mathematics, University of Iowa, Iowa City, IA 52242, USA.

The Journal of Supercomputing

|July 13, 2010

Summary

This summary is machine-generated.

This study introduces a novel parallel method for computed tomography (CT) image reconstruction, significantly reducing computational time for noisy or truncated data. The innovative approach enhances reconstruction speed and efficiency in medical imaging applications.

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Synchronous Triplanar Reconstruction Integrated with Color Doppler Mapping for Precise and Rapid Localization of Thyroid Lesions

Synchronous Triplanar Reconstruction Integrated with Color Doppler Mapping for Precise and Rapid Localization of Thyroid Lesions

Published on: February 9, 2024

Related Experiment Videos

Last Updated: Jun 11, 2026

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

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

Published on: November 23, 2019

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Synchronous Triplanar Reconstruction Integrated with Color Doppler Mapping for Precise and Rapid Localization of Thyroid Lesions

Synchronous Triplanar Reconstruction Integrated with Color Doppler Mapping for Precise and Rapid Localization of Thyroid Lesions

Published on: February 9, 2024

Area of Science:

Medical Imaging
Computational Science

Background:

Iterative algorithms are effective for CT image reconstruction but computationally intensive.
Existing parallel techniques face communication overhead challenges, limiting performance.

Purpose of the Study:

To develop an innovative parallel method for CT image reconstruction.
To overcome the computational time and communication overhead limitations of existing methods.

Main Methods:

Proposed a parallel method based on local iterative CT reconstruction.
Partitioned the object into subregions assigned to different processing elements (PEs).
Performed local iterative reconstruction within each PE.

Main Results:

Numerical experiments on a high-performance computing cluster demonstrated significant reduction in reconstruction time.
Achieved high speedup and efficiency using the FORBILD head phantom as a benchmark.
The proposed algorithm effectively addresses the computational demands of CT image reconstruction.

Conclusions:

The innovative parallel CT reconstruction algorithm offers substantial improvements in speed and efficiency.
This method is well-suited for reconstructing CT images from noisy or truncated projection data.
The approach shows promise for accelerating medical imaging workflows.