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

Astrocytic Ferroptosis: An Integrative Hub Linking Metabolic Dyshomeostasis, Glial Crosstalk, and Neurodegeneration in Alzheimer's Disease.

Aging and disease·2026

Same author

Oligodendrocyte dysfunction in neurodegenerative diseases: pathological features, underlying mechanisms and therapeutic targeting.

Frontiers in aging neuroscience·2026

Same author

Long cardiac troponin T in myocardial infarction: a promising diagnostic tool, a precision imperative.

European heart journal·2026

Same author

Elevating Ventilator Mode Research: Key Methodologic Tweaks for Precision in Critical Care.

Chest·2026

Same author

Letter by Meng et al Regarding Article, "An Aberrant Resurgence of Endogenous Retroviruses Prompts Myocarditis and Heart Failure".

Circulation·2026

Same author

Letter Regarding: Prehospital Experience With Left Ventricular Assist Devices.

The Journal of surgical research·2026

Same journal

Column reduced digital nets.

Numerical algorithms·2026

Same journal

Vem++, a C++ library to handle and play with the virtual element method.

Numerical algorithms·2026

Same journal

The detection and correction of silent errors in pipelined Krylov subspace methods.

Numerical algorithms·2026

Same journal

Optimized Schwarz waveform relaxation methods for wave-heat coupling in one dimensional bounded domains.

Numerical algorithms·2025

Same journal

Randomized methods for computing joint eigenvalues, with applications to multiparameter eigenvalue problems and root finding.

Numerical algorithms·2025

Same journal

Patch area and uniform sampling on the surface of any ellipsoid.

Numerical algorithms·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 16, 2025

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

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

Published on: November 23, 2019

Numerical methods for CT reconstruction with unknown geometry parameters.

Chang Meng¹, James Nagy¹

¹Department of Mathematics, Emory University, 400 Dowman Drive, Atlanta, GA 30322 USA.

Numerical Algorithms

|December 26, 2022

Summary

This summary is machine-generated.

Portable computed tomography (CT) machines face calibration challenges. A new hybrid machine learning and block coordinate descent (ML-BCD) method accurately estimates geometry parameters and reconstructs images simultaneously.

Keywords:

Computed tomography Machine learning Optimization

More Related Videos

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Published on: February 21, 2025

Related Experiment Videos

Last Updated: Aug 16, 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 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Published on: February 21, 2025

Area of Science:

Medical Imaging
Computational Imaging
Applied Mathematics

Background:

Standard computed tomography (CT) systems offer high-quality medical diagnostics but are large, costly, and require extensive calibration.
Portable CT devices present challenges in precise geometric parameter calibration, potentially impacting image quality due to parameter drift during acquisition.

Purpose of the Study:

To address the non-linear inverse problem in portable CT imaging.
To develop algorithms for joint estimation of geometry parameters and image reconstruction.

Main Methods:

Proposed a hybrid machine learning and block coordinate descent (ML-BCD) approach.
Utilized a machine learning model for initial geometry parameter calibration.
Employed block coordinate descent for simultaneous parameter refinement and image reconstruction.

Main Results:

The ML-BCD method efficiently improved the accuracy of reconstructed images.
Demonstrated enhanced precision in estimating the geometry parameters of portable CT devices.
Numerical experiments validated the effectiveness of the proposed joint estimation approach.

Conclusions:

The ML-BCD method offers a viable solution for accurate image reconstruction and geometry calibration in portable CT systems.
This approach enhances the diagnostic utility of portable CT devices by overcoming inherent calibration limitations.
The study highlights the potential of integrating machine learning with optimization techniques for advanced medical imaging applications.