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Related Concept Videos

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

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

133
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...
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Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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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...
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Related Experiment Video

Updated: Sep 20, 2025

Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection
06:57

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Generative models for reproducible coronary calcium scoring.

Sanne G M van Velzen1,2,3,4, Bob D de Vos1,2,3, Julia M H Noothout1,2,3

  • 1Amsterdam UMC location University of Amsterdam, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands.

Journal of Medical Imaging (Bellingham, Wash.)
|June 6, 2022
PubMed
Summary

A new generative adversarial network (GAN) method improves coronary artery calcium (CAC) quantification by eliminating the need for intensity thresholds. This enhances reproducibility for more reliable coronary heart disease risk assessment.

Keywords:
calcium scoringcomputed tomographycycle-consistent generative adversarial networkgenerative modelsreproducibility

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Cardiovascular Imaging

Background:

  • Coronary artery calcium (CAC) scoring is a key predictor of coronary heart disease (CHD) events.
  • Current CAC scoring methods have limited interscan reproducibility due to fixed intensity thresholds, especially in non-ECG-synchronized CT.
  • Cardiac motion and partial volume effects exacerbate reproducibility issues.

Purpose of the Study:

  • To develop and evaluate a novel CAC quantification method that does not require a segmentation threshold.
  • To improve the interscan reproducibility of CAC scoring.
  • To enhance the reliability of CHD risk categorization and prediction accuracy.

Main Methods:

  • Utilized a cycle-consistent generative adversarial network (GAN) to decompose CT images into CAC and non-CAC components.
  • Trained the GAN model using a dataset of 626 low-dose chest CTs and 514 radiotherapy treatment planning (RTP) CTs.
  • Compared interscan reproducibility of the proposed method against clinical calcium scoring in 1662 patients with two scans each.

Main Results:

  • The proposed GAN method achieved a significantly lower relative interscan difference in CAC mass (47%) compared to manual clinical scoring (89%).
  • The intraclass correlation coefficient for Agatston scores was 0.96 with the proposed method, versus 0.91 for automatic clinical scoring.
  • Demonstrated superior interscan reproducibility for CAC quantification.

Conclusions:

  • The developed GAN-based CAC quantification method offers improved interscan reproducibility.
  • Enhanced reproducibility can lead to more reliable CHD risk stratification.
  • This method holds potential for improving the accuracy of CHD event prediction.