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Updated: May 28, 2026

Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
04:40

Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans

Published on: August 28, 2018

Advanced Quantitative CT for Coronary Artery Disease: Integrating Stenosis-, Plaque Quantification, and FFR-CT

Samuel Mihalcioiu1, Bethlehem Mengesha1, Chen Abitbol1

  • 1Department of Radiology, St Paul's Hospital & University of British Columbia, Vancouver, BC, Canada.

The British Journal of Radiology
|May 26, 2026
PubMed

Insights

Newer cardiac computed tomographic angiography (CCTA) techniques, including AI-QCT and PCD-CT, improve coronary artery disease (CAD) evaluation. These advancements enhance plaque quantification and stenosis assessment for better risk stratification of major adverse cardiovascular events (MACE).

Area of Science:

  • Cardiovascular imaging and radiology
  • Medical artificial intelligence
  • Cardiology

Background:

  • Coronary artery disease (CAD) remains a primary global cause of mortality.
  • Current radiological evaluation of CAD using cardiac computed tomographic angiography (CCTA) for stenosis detection faces challenges in efficiency and inter-observer consistency.
  • Accurate risk stratification for major adverse cardiovascular events (MACE) is crucial for effective patient management.

Purpose of the Study:

  • To review the latest advancements in CCTA for CAD evaluation, focusing on plaque quantification.
  • To highlight the role of artificial intelligence (AI) and novel CT technologies in improving CAD assessment.
  • To discuss the impact of these innovations on risk stratification and preventive therapy.

Main Methods:

  • Exploration of artificial intelligence-based quantitative computed tomography (AI-QCT) for plaque biomarker analysis.
  • Review of artificial intelligence-based coronary stenosis quantification (AI-CSQ) for workflow optimization.
  • Discussion of photon counting detector CT (PCD-CT) for enhanced spatial resolution and artifact reduction.
  • Inclusion of fractional flow reserve-computed tomography (FFR-CT) for non-invasive physiological assessment.

Main Results:

  • AI-QCT provides efficient and reproducible plaque analysis for refined MACE risk-stratification.
  • AI-CSQ streamlines workflow and improves consistency in stenosis quantification.
  • PCD-CT offers enhanced precision in coronary lumen analysis and artifact reduction.
  • FFR-CT enables non-invasive physiological assessment of stenotic lesions.

Conclusions:

  • Emerging CCTA technologies, including AI and PCD-CT, significantly enhance the evaluation of coronary artery disease.
  • These advancements offer more precise plaque quantification and stenosis assessment, leading to improved risk stratification for MACE.
  • Future directions point towards further integration of these technologies for guiding preventive therapies in cardiology.

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