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

4.2K
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...
4.2K

You might also read

Related Articles

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

Sort by
Same author

TCRBinder: Unified pre-trained language model with paired-chain synergy for predicting T-cell receptor binding specificity.

PLoS computational biology·2026
Same author

Adaptive feature unlearning for trustworthy medical imaging privacy.

Medical image analysis·2026
Same author

Deep learning-based Breast Imaging Reporting and Data System classification and establishment of diagnostic model in breast cancer diagnosis with automated breast ultrasound.

Quantitative imaging in medicine and surgery·2026
Same author

Grading and detecting gastroenteropancreatic neuroendocrine neoplasms with dual-tracer ( 18 F-AlF-NOTA-octreotide/ 18 F-FDG) PET/CT: a metabolic parameter-based study.

Nuclear medicine communications·2026
Same author

Clinical and Dermoscopic Features of Childhood Flexural Comedones: An Analysis of 19 Pediatric Cases.

International journal of dermatology·2026
Same author

Anatomy-Guided Spatiotemporal Affinity Learning for Unsupervised Domain Adaptation in Echocardiography Segmentation.

IEEE journal of biomedical and health informatics·2026

Related Experiment Video

Updated: Jun 3, 2025

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

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

Published on: October 13, 2023

1.4K

Convergent-Diffusion Denoising Model for multi-scenario CT Image Reconstruction.

Xinghua Ma1, Mingye Zou2, Xinyan Fang2

  • 1The Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang, China; The Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Makkah, Saudi Arabia.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|January 9, 2025
PubMed
Summary
This summary is machine-generated.

A new Convergent-Diffusion Denoising Model (CDDM) enhances CT image reconstruction (CTIR) by reducing noise in various scenarios like low-dose CT denoising (LDCTD) and sparse-view CT reconstruction (SVCTR). This versatile approach improves diagnostic accuracy across diverse patient cases.

Keywords:
Diffusion-based modelDual-domainImage reconstructionLow-dose CTMetal artifactMulti-scenarioSinogramSparse-view CT

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

9.7K
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

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

Published on: November 23, 2019

7.9K

Related Experiment Videos

Last Updated: Jun 3, 2025

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

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

Published on: October 13, 2023

1.4K
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

9.7K
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

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

Published on: November 23, 2019

7.9K

Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Image Processing

Background:

  • Current CT image reconstruction (CTIR) techniques often specialize in specific tasks like low-dose CT denoising (LDCTD), sparse-view CT reconstruction (SVCTR), and metal artifact reduction (MAR).
  • These specialized techniques exhibit limited generalization capabilities across diverse CT imaging scenarios due to their narrow focus.

Purpose of the Study:

  • To propose a novel, versatile CT image reconstruction (CTIR) scheme with high generalization capabilities for multi-scenario applications.
  • To develop a Convergent-Diffusion Denoising Model (CDDM) that mitigates imaging noise and enhances diagnostic dependability in CT imaging.

Main Methods:

  • A novel Convergent-Diffusion Denoising Model (CDDM) employing a stepwise denoising process based on a priori decay distribution to achieve noise-free images.
  • Integration of a domain-correlated sampling network (DS-Net) for sinogram-guided noise prediction, utilizing dual-domain information (image and sinogram).
  • DS-Net analyzes dual-domain correlations to sample noise distribution, incorporating sinogram semantics to prevent secondary artifacts.

Main Results:

  • The proposed CDDM scheme demonstrates practical applicability across various CTIR scenarios, including LDCTD, MAR, and SVCTR.
  • The model effectively mitigates imaging noise, improving the dependability of CT imaging diagnostics.
  • Sinogram knowledge integration within the DS-Net successfully avoids secondary artifacts.

Conclusions:

  • The Convergent-Diffusion Denoising Model (CDDM) offers a versatile and generalizable solution for multi-scenario CT image reconstruction.
  • Leveraging dual-domain information and sinogram semantics significantly enhances noise reduction and artifact mitigation in CT imaging.
  • The proposed scheme improves diagnostic accuracy and broadens the applicability of CT imaging across various clinical situations.