Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Computed Tomography01:10

Computed Tomography

9.3K
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...
9.3K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

558
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...
558
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

486
Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
486
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

3.0K
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...
3.0K
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

1.1K
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...
1.1K
Positron Emission Tomography01:29

Positron Emission Tomography

7.9K
Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
7.9K

You might also read

Related Articles

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

Sort by
Same author

No association between troponin and COPD without cardiovascular influence: findings from a population-based cohort (SCAPIS).

Thorax·2026
Same author

Can the current radiation dose, in chest tomosynthesis, be reduced with retained image quality? A study in the context of lung cancer screening programs.

PloS one·2026
Same author

Survey of radiological optimization processes in Swedish hospitals-similarities and differences between different modalities.

Radiation protection dosimetry·2026
Same author

Assessing the influence of kernel selection on chest computed tomography image quality across varying dose levels using TrueFidelity reconstruction.

Radiation protection dosimetry·2026
Same author

Optimisation in X-ray and molecular imaging 2025-editorial.

Radiation protection dosimetry·2026
Same author

Modelling the relative precision of whole-kidney dosimetry in molecular radiotherapy using a power law approach.

Radiation protection dosimetry·2026

Related Experiment Video

Updated: Mar 14, 2026

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
03:55

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer

Published on: June 9, 2023

1.1K

From digital chest tomosynthesis to 3D CT.

Attila Simkó1, Patrik Sund1,2, Maral Mirzai1,2

  • 1Department of Biomedical Engineering and Medical Physics, Sahlgrenska University Hospital, Region Västra Götaland, SE-413 45, Gothenburg, Sweden.

Radiation Protection Dosimetry
|March 13, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient deep learning method for reconstructing 3D imaging from digital chest tomosynthesis data. The approach reconstructs sagittal CT slices, offering a promising direction for low-resource volumetric imaging.

More Related Videos

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging
09:32

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging

Published on: December 9, 2021

3.6K
3D Printing of Preclinical X-ray Computed Tomographic Data Sets
11:06

3D Printing of Preclinical X-ray Computed Tomographic Data Sets

Published on: March 22, 2013

41.1K

Related Experiment Videos

Last Updated: Mar 14, 2026

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
03:55

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer

Published on: June 9, 2023

1.1K
Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging
09:32

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging

Published on: December 9, 2021

3.6K
3D Printing of Preclinical X-ray Computed Tomographic Data Sets
11:06

3D Printing of Preclinical X-ray Computed Tomographic Data Sets

Published on: March 22, 2013

41.1K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology

Background:

  • Digital chest tomosynthesis (DCT) reconstructs 3D volumes from limited-angle low-dose projections.
  • DCT reconstructions exhibit lower depth resolution and are susceptible to motion artifacts compared to computed tomography (CT).
  • Current deep learning methods for full-resolution CT reconstruction from projections are computationally intensive.

Purpose of the Study:

  • To develop a more computationally efficient deep learning framework for reconstructing volumetric imaging from tomosynthesis data.
  • To explore a novel approach for tomosynthesis-based volumetric imaging with reduced memory demands.

Main Methods:

  • A deep learning framework was developed to reconstruct sagittal CT slices from small patches of projection data.
  • The model segments voxels into air, soft tissue, or bone classes instead of predicting continuous Hounsfield Unit (HU) values.
  • This patch-based, segmentation-focused approach significantly lowers memory requirements.

Main Results:

  • The method successfully captures coarse structural features and depth information with high consistency.
  • Reconstruction of fine details remains a challenge.
  • The approach demonstrates potential for low-resource tomosynthesis-based volumetric imaging.

Conclusions:

  • The proposed deep learning framework offers a computationally efficient alternative for volumetric reconstruction from digital chest tomosynthesis.
  • While not yet clinically deployable due to limitations in fine detail reconstruction, the method presents a promising avenue for future research in resource-constrained medical imaging.