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...

You might also read

Related Articles

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

Sort by

Same author

Acute Anterior Choroidal Artery Territory Infarction: A Retrospective Study.

Clinical neurology and neurosurgery·2020

Same author

Preference Ranking Procedure: Method Validation with Dogs.

Animals : an open access journal from MDPI·2020

Same author

Controlling defects in crystalline carbon nitride to optimize photocatalytic CO<sub>2</sub> reduction.

Chemical communications (Cambridge, England)·2020

Same author

Fabrication and Fireproofing Performance of the Coal Fly Ash-Metakaolin-Based Geopolymer Foams.

Materials (Basel, Switzerland)·2020

Same author

High-Resolution Chest X-Ray Bone Suppression Using Unpaired CT Structural Priors.

IEEE transactions on medical imaging·2020

Same author

Facile green synthesis of calcium carbonate/folate porous hollow spheres for the targeted pH-responsive release of anticancer drugs.

Journal of materials chemistry. B·2020

Same journal

Generative morphodynamic forecasting enables robust zero-shot volumetric medical segmentation.

Medical image analysis·2026

Same journal

ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism.

Medical image analysis·2026

Same journal

MedP-CLIP: Medical CLIP with region-aware prompt integration.

Medical image analysis·2026

Same journal

Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation.

Medical image analysis·2026

Same journal

SUDA: Simultaneous unsupervised knowledge distillation and adaptation of foundation models for efficient pathological image analysis.

Medical image analysis·2026

Same journal

Beyond the LUMIR challenge: The pathway to foundational registration models.

Medical image analysis·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 10, 2025

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

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

Published on: October 24, 2019

3DGR-CT: Sparse-view CT reconstruction with a 3D Gaussian representation.

Yingtai Li¹, Xueming Fu¹, Han Li¹

¹School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC), Hefei, 230026, Anhui, China; China and Center for Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE), Suzhou Institute for Advance Research, USTC, Suzhou, 215123, Jiangsu, China.

Medical Image Analysis

|April 25, 2025

Summary

This summary is machine-generated.

A new 3D Gaussian representation (3DGR) method enhances sparse-view computed tomography (CT) reconstruction. This approach improves image quality and speed, offering a promising alternative for low-dose medical imaging.

Keywords:

3D Gaussian representation CT reconstruction Sparse-view computed tomography

More Related Videos

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

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

Published on: October 13, 2023

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

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

Published on: November 23, 2019

Related Experiment Videos

Last Updated: May 10, 2025

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

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

Published on: October 24, 2019

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

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

Published on: October 13, 2023

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

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

Published on: November 23, 2019

Area of Science:

Medical Imaging
Computational Imaging
Computer Vision

Background:

Sparse-view computed tomography (CT) minimizes radiation exposure but suffers from noise and artifacts due to limited projection data.
Existing reconstruction methods struggle to balance image quality and computational efficiency.

Purpose of the Study:

To introduce a novel 3D Gaussian representation (3DGR) based method for accurate and efficient sparse-view CT reconstruction.
To address the limitations of current sparse-view CT techniques by leveraging advanced neural representation.

Main Methods:

Developed a 3D Gaussian representation (3DGR) for CT reconstruction, inspired by 3D Gaussian splatting.
Introduced FBP-image-guided Gaussian initialization for improved starting points.
Integrated the 3DGR with a differentiable CT projector for end-to-end optimization.

Main Results:

The proposed 3DGR-CT method significantly outperforms state-of-the-art methods in reconstruction accuracy.
Demonstrated faster convergence and improved efficiency compared to existing techniques.
Showcased potential for real-time physical simulation in CT imaging.

Conclusions:

3D Gaussian representation offers an effective and efficient alternative to implicit neural representations for sparse-view CT.
The proposed 3DGR-CT method achieves superior performance in sparse-view CT reconstruction.
This technique has significant potential for clinical applications requiring low-dose CT and real-time simulation.