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

7.9K
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...
7.9K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

268
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...
268
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

764
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...
764

You might also read

Related Articles

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

Sort by
Same journal

Simulation-driven deep learning for the diagnosis of middle ear pathologies using wideband acoustic immittance.

Biomedical physics & engineering express·2026
Same journal

Unsupervised machine learning-assisted multimodal characterization of cardiomyocytes on a thin-film-transistor microelectrode array (TFT-MEA).

Biomedical physics & engineering express·2026
Same journal

Assessment of experimental values of effective energies and beam quality correction factors for out-of-field dosimetry in external beam radiotherapy using radiophotoluminescent glass dosimeters.

Biomedical physics & engineering express·2026
Same journal

Machine learning-driven correction of handgrip strength: a novel biomarker for neurological and health outcomes in the UK Biobank.

Biomedical physics & engineering express·2026
Same journal

A numerical study on the equivalence of complete and shunt electrode models for transfer impedance in electrical impedance tomography.

Biomedical physics & engineering express·2026
Same journal

Dynamics of endothelial intercellular junctions under ultrasonic cavitation and application in drug delivery.

Biomedical physics & engineering express·2026

Related Experiment Video

Updated: Jan 11, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K

Metaheuristic-optimized generative adversarial network for enhanced sparse-view low-dose CT reconstruction.

Jafar Majidpour1, Hakem Beitollahi1

  • 1Department of Computer Science, Faculty of Science, Soran University, Soran, Kurdistan Region, Iraq.

Biomedical Physics & Engineering Express
|November 19, 2025
PubMed
Summary
This summary is machine-generated.

This study enhances low-dose computed tomography (LDCT) image quality using AI. A Pix2Pix conditional generative adversarial network (CGAN) with metaheuristic optimization significantly improves sparse-view CT reconstruction, balancing image integrity and computational efficiency.

Keywords:
CSDEPSOPix2Pixartifactsreconstructed imagessparse-view CT

Related Experiment Videos

Last Updated: Jan 11, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Science

Background:

  • Sparse-view low-dose computed tomography (LDCT) faces challenges in maintaining image quality while minimizing radiation exposure.
  • Artificial intelligence (AI) is increasingly utilized to address artifacts in LDCT imaging.
  • Reconstructing high-quality CT images from limited projection data remains a significant hurdle.

Purpose of the Study:

  • To develop and evaluate an AI-driven method for enhancing sparse-view CT image reconstruction.
  • To integrate a conditional generative adversarial network (CGAN) with metaheuristic optimization techniques.
  • To improve the quality of sparse-view LDCT images by optimizing key hyperparameters.

Main Methods:

  • A Pix2Pix CGAN model was employed for image reconstruction.
  • Metaheuristic optimization algorithms, including Particle Swarm Optimization (PSO), Differential Evolution (DE), and Cuckoo Search (CS), were used to tune hyperparameters like learning rate and beta values.
  • The method was assessed using the LDCT-P and LUNA16 datasets across various sparse-view configurations (10 to 512 views).

Main Results:

  • Image quality significantly improved with an increasing number of view projections.
  • The Pix2Pix + PSO combination showed superior performance, with Structural Similarity Index (SSIM) scores increasing from 0.900 to 0.972 for abdominal CT and 0.801 to 0.971 for lung CT.
  • The optimized CGAN model demonstrated effective artifact reduction and image detail preservation.

Conclusions:

  • The integration of Pix2Pix CGAN with metaheuristic optimization offers a robust solution for sparse-view CT image enhancement.
  • This approach successfully balances computational efficiency with high image integrity.
  • The developed method holds promise for advancing clinical applications of LDCT imaging.