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

Image Reconstruction with Maclaurin Series Expansion.

International journal of biomedical research & practice·2026

Same author

A Higher-Order Ising Model with Gradient-Free Update.

Axioms·2026

Same author

Limited-Angle Tomography Using a Neural Network as the Objective Function.

International journal of biomedical research & practice·2026

Same author

Radon Inversion Reconstruction for Kooshball-Like Sampling Trajectory in Cine.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same author

Mitigating the Drawbacks of the L<sub>0</sub> Norm and the Total Variation Norm.

Axioms·2025

Same author

One-Step Image Reconstruction for Cine MRI with a Quadratic Constraint.

International journal of biomedical research & practice·2024

Same journal

Morphing from the TV-Norm to the <i>l</i> <sub></sub> -Norm.

Biomedical journal of scientific & technical research·2024

Same journal

An Integrative Genomics Approach for Associating Genetic Susceptibility with the Tumor Immune Microenvironment in Triple Negative Breast Cancer.

Biomedical journal of scientific & technical research·2024

Same journal

Machine-learning Modeling for Personalized Immunotherapy- An Evaluation Module.

Biomedical journal of scientific & technical research·2023

Same journal

Closing the Gaps on Medical Education in Low-Income Countries Through Information & Communication Technologies: The Mozambique Experience.

Biomedical journal of scientific & technical research·2023

Same journal

Immunomodulatory Strategies for Spinal Cord Injury.

Biomedical journal of scientific & technical research·2023

Same journal

Estrogen Sulfotransferase Induction Inhibits Breast Cancer Cell Line MCF-7 Proliferation.

Biomedical journal of scientific & technical research·2022

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 23, 2025

Non-invasive 3D-Visualization with Sub-micron Resolution Using Synchrotron-X-ray-tomography

Non-invasive 3D-Visualization with Sub-micron Resolution Using Synchrotron-X-ray-tomography

Published on: May 27, 2008

Extreme Few-View Tomography without Training Data.

Gengsheng L Zeng^1,2

¹Department of Computer Science, Utah Valley University, USA.

Biomedical Journal of Scientific & Technical Research

|June 17, 2024

Summary

This summary is machine-generated.

Reconstructing images from extreme few-view tomography is challenging. This study introduces using linear attenuation coefficients as extra constraints, significantly improving image reconstruction quality beyond current compressed sensing methods.

Keywords:

Few-View Tomography Image Reconstruction Inverse Problem Iterative Algorithm Optimization Total-Variation Minimization

More Related Videos

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Published on: August 16, 2012

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Related Experiment Videos

Last Updated: Jun 23, 2025

Non-invasive 3D-Visualization with Sub-micron Resolution Using Synchrotron-X-ray-tomography

Non-invasive 3D-Visualization with Sub-micron Resolution Using Synchrotron-X-ray-tomography

Published on: May 27, 2008

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Published on: August 16, 2012

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Area of Science:

Medical Imaging
Computational Imaging
Applied Mathematics

Background:

Extreme few-view tomography involves reconstructing images from fewer than 10 projection views.
Current state-of-the-art methods rely on compressed sensing (CS) techniques, utilizing sparsification transformations and total variation (TV) norm minimization.
Standard CS methods are often insufficient for the severe data limitations in extreme few-view scenarios.

Purpose of the Study:

To investigate the feasibility and effectiveness of incorporating additional prior information as constraints for extreme few-view tomography.
To enhance image reconstruction quality in scenarios with highly limited projection data.
To address the limitations of existing compressed sensing approaches in extreme few-view conditions.

Main Methods:

The study proposes using prior knowledge of linear attenuation coefficients of the imaged objects as supplementary constraints.
These known attenuation values are integrated into the reconstruction process to guide the solution.
The methodology was evaluated using computer simulations of transmission tomography.

Main Results:

The incorporation of linear attenuation coefficients as extra constraints demonstrated a significant improvement in image reconstruction quality.
The proposed method showed enhanced performance compared to conventional compressed sensing techniques under extreme few-view conditions.
Computer simulations confirmed the utility of these additional constraints in improving tomographic reconstructions.

Conclusions:

Utilizing prior knowledge of linear attenuation coefficients is a viable strategy to overcome the challenges of extreme few-view tomography.
This approach offers a practical enhancement for image reconstruction when projection data is severely limited.
The findings suggest a promising direction for improving imaging capabilities in applications where acquiring numerous views is difficult or impossible.