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Related Concept Videos

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

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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

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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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Artificial intelligence-powered automatic coronary computed tomography angiography plaque quantification: comparison

Guanyu Li1, Wei Yu1, Zhiqing Wang1,2

  • 1Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.

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|March 9, 2026
PubMed
Summary

An AI tool accurately quantifies coronary artery plaque using coronary computed tomography angiography (CCTA), correlating well with optical coherence tomography (OCT). This AI method aids in identifying vulnerable plaques and improves CCTA interpretation for coronary artery disease.

Keywords:
Artificial intelligenceAutomatic co-registrationCoronary computed tomography angiographyOptical coherence tomographyPlaque characterizationPlaque vulnerability

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Area of Science:

  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine
  • Medical Diagnostics

Background:

  • Coronary computed tomography angiography (CCTA) is a key non-invasive tool for assessing coronary artery disease (CAD).
  • Artificial intelligence (AI) has the potential to enhance the interpretation of CCTA images, particularly in plaque quantification.
  • Accurate plaque characterization is crucial for predicting cardiovascular events.

Purpose of the Study:

  • To evaluate an AI-powered method for automatic plaque quantification from CCTA.
  • To compare AI-derived plaque quantification with optical coherence tomography (OCT) as the reference standard.
  • To assess the performance of AI in identifying high-risk plaque features and vulnerable plaques.

Main Methods:

  • Retrospective analysis of patients who underwent both CCTA and OCT.
  • AI-assisted automatic plaque quantification and composition classification on CCTA using adaptive Hounsfield unit thresholds.
  • Automated co-registration of CCTA and OCT data.
  • Evaluation of 91 patients with 153 co-registered lesions.

Main Results:

  • AI-assisted CCTA plaque quantification showed significant correlations with OCT for plaque volume (r=0.84), plaque burden, and composition (all P<0.001).
  • AI identified independent predictors of OCT-derived vulnerable plaques, including CCTA-derived plaque volume, maximal plaque burden, lipidic tissue volume, and high-risk plaque features.
  • The average time for AI-based plaque quantification was 1.8 minutes per patient.

Conclusions:

  • A novel AI-powered method enables fully automatic plaque quantification from CCTA.
  • The AI method demonstrates strong correlation with OCT, supporting its utility in clinical practice.
  • This AI tool has the potential to improve the efficiency and accuracy of coronary artery disease assessment using CCTA.