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

Imaging Studies for Cardiovascular System V: CT

Imaging Studies for Cardiovascular System V: CT

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...

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

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...

You might also read

Related Articles

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

Sort by

Same author

Factors Associated with Influenza Vaccination Among Children Aged 6 Months to 12 Years - Three Provinces, China, 2024-2025 Influenza Season.

China CDC weekly·2026

Same author

Metagenomic next-generation sequencing-guided management of descending mediastinitis and empyema caused by Segatella baroniae: a case report.

BMC pulmonary medicine·2026

Same author

MOFs mixed matrix membranes for CO<sub>2</sub> separation: material design, optimization strategies, and industrial pathways.

Environmental research·2026

Same author

Integrated transcriptomics identifies ER stress-associated apoptosis in post-resuscitation AKI and supports early Dl-3-n-butylphthalide-associated renoprotection in a porcine TCA model.

Frontiers in pharmacology·2026

Same author

Reduced left dorsolateral prefrontal activation and right inferior frontal de-oxygenation differ between psychotic and non-psychotic adolescent depression during verbal fluency.

Frontiers in psychiatry·2026

Same author

Distinct associations of pioneer factor Ascl1-E12a with nucleosomes drive changes in cell fate.

Molecular cell·2026

Search research articles

Related Experiment Video

Updated: Jan 16, 2026

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Published on: February 21, 2025

Deep Learning-Based Cardiac CT Coronary Motion Correction Method with Temporal Weight Adjustment: Clinical Data

Dan Yao¹, Chengxi Yan², Wang Du¹

¹Department of Research Institute, Beijing Wandong Medical Technology Ltd., Beijing, 100016, China.

Journal of Imaging Informatics in Medicine

|October 1, 2025

Summary

A new deep learning method, the temporal-weighted motion correction network (TW-MoCoNet), significantly reduces cardiac motion artifacts in coronary CT angiography (CCTA) images. This improves image clarity and aids radiologists in accurately evaluating coronary vessels.

Keywords:

Cardiac CTA Clinical data evaluation Motion correction Temporal weight adjustment

More Related Videos

Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection

Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection

Published on: September 22, 2023

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Related Experiment Videos

Last Updated: Jan 16, 2026

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Published on: February 21, 2025

Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection

Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection

Published on: September 22, 2023

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Area of Science:

Medical Imaging
Artificial Intelligence
Cardiovascular Imaging

Background:

Cardiac motion artifacts degrade coronary computed tomography angiography (CCTA) image quality.
This degradation hinders accurate identification and evaluation of coronary vessels by radiologists.

Purpose of the Study:

To propose a deep learning-based method for coronary artery motion compensation.
To enhance the interpretability of CCTA images affected by motion artifacts.

Main Methods:

Developed a temporal-weighted motion correction network (TW-MoCoNet) using a deep learning approach.
Generated motion artifact data via simulation for network training.
Trained TW-MoCoNet with paired no-artifact and artifact images.

Main Results:

Evaluated TW-MoCoNet on 67 clinical CCTA datasets using objective and subjective metrics.
Demonstrated substantial improvements in image quality, with a decrease in moderately artifacted segments by 80.2%.
Achieved 50.0% artifact-free segments, indicating significant clinical relevance.

Conclusions:

The proposed TW-MoCoNet effectively reduces motion artifacts in CCTA images.
The method enhances image clarity and clinical interpretability for coronary vessel evaluation.
This deep learning approach assists clinicians in accurate diagnosis and assessment.