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

4.6K
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...
4.6K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

29
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...
29
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.4K
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...
2.4K
Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

42
Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
42

You might also read

Related Articles

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

Sort by
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 author

Design optimization of a periodic microstructured array anode for hard x-ray grating interferometry.

Physics in medicine and biology·2019
Same journal

Poisoning the Genome: Targeted Backdoor Attacks on DNA Foundation Models.

ArXiv·2026
Same journal

Mechanistic mathematical model of the in vitro infection dynamics of Bunyamwera and Batai viruses including MOI-dependent shortening of the eclipse phase.

ArXiv·2026
Same journal

AI-Driven Lumped-Element Modeling of Human Respiratory System for Studying Voice Mechanics.

ArXiv·2026
Same journal

Beyond Algorithms: Conceptual Innovation in Medical Imaging AI.

ArXiv·2026
Same journal

Feynman Kac Reweighted Schrödinger Bridge Matching for Surface-Based Tau PET Harmonization.

ArXiv·2026
Same journal

Agentic Discovery of Non-Canonical Antimicrobial Peptides with AMPGAN v3.

ArXiv·2026
See all related articles

Related Experiment Video

Updated: Jul 24, 2025

Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo
12:54

Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo

Published on: October 2, 2021

3.3K

Tomographic Image Reconstruction Using an Advanced Score Function (ADSF).

Wenxiang Cong1, Wenjun Xia1, Ge Wang1

  • 1Biomedical Imaging Center, Center for Computational Innovations, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180.

Arxiv
|July 3, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced score function (ADSF) for computed tomography (CT) image reconstruction. The novel deep learning-based method significantly reduces noise and artifacts in low-dose CT scans.

Keywords:
Computed tomography (CT)Gaussian mixture modeldeep learningimage reconstructionmaximum a posteriori (MAP) estimationradiation dose reductionscore function

More Related Videos

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

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

Published on: October 24, 2019

9.9K
Using Synchrotron Radiation Microtomography to Investigate Multi-scale Three-dimensional Microelectronic Packages
08:46

Using Synchrotron Radiation Microtomography to Investigate Multi-scale Three-dimensional Microelectronic Packages

Published on: April 13, 2016

10.1K

Related Experiment Videos

Last Updated: Jul 24, 2025

Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo
12:54

Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo

Published on: October 2, 2021

3.3K
3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

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

Published on: October 24, 2019

9.9K
Using Synchrotron Radiation Microtomography to Investigate Multi-scale Three-dimensional Microelectronic Packages
08:46

Using Synchrotron Radiation Microtomography to Investigate Multi-scale Three-dimensional Microelectronic Packages

Published on: April 13, 2016

10.1K

Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Artificial Intelligence

Background:

  • Computed tomography (CT) image reconstruction from limited X-ray projection data (low-dose or sparse-view) often results in significant noise and artifacts.
  • Classic reconstruction algorithms struggle to produce high-quality images under these challenging acquisition conditions.
  • Maximum a posteriori (MAP) estimation offers a framework for incorporating image priors to improve reconstruction quality.

Approach:

  • Developed a novel iterative image reconstruction method based on MAP estimation.
  • Leveraged deep learning and Gaussian mixture models to derive an advanced score function (ADSF) for image prior.
  • Integrated the ADSF into the iterative reconstruction process, creating the ADSF iterative reconstruction method.

Key Points:

  • The ADSF method significantly improves image reconstruction quality by effectively reducing noise and artifacts.
  • Mathematical analysis confirms the convergence of the ADSF iterative reconstruction algorithm.
  • Evaluated performance on public medical image and clinical CT datasets, demonstrating superior denoising and deblurring capabilities.

Conclusions:

  • The proposed ADSF iterative reconstruction method offers enhanced image quality for low-dose and sparse-view CT.
  • The method demonstrates excellent generalizability and stability across different datasets.
  • This deep learning-based approach represents a significant advancement in medical image reconstruction technology.