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Related Experiment Video

Updated: Jun 9, 2025

Ultrasound Based Assessment of Coronary Artery Flow and Coronary Flow Reserve Using the Pressure Overload Model in Mice
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Diagnostic performance of fully automatic coronary CT angiography-based quantitative flow ratio.

Guanyu Li1, Tingwen Weng2, Pengcheng Sun1

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

Journal of Cardiovascular Computed Tomography
|October 24, 2024
PubMed
Summary

An artificial intelligence method for automatic coronary computed tomography angiography (CCTA) reconstruction and computed quantitative flow ratio (CT-μFR) computation demonstrated high feasibility. This AI-powered CT-μFR accurately identifies significant coronary artery stenosis before invasive procedures.

Keywords:
Artificial intelligenceCoronary computed tomography angiographyFractional flow reserveQuantitative flow ratio

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Area of Science:

  • Cardiovascular Imaging and Intervention
  • Artificial Intelligence in Medical Diagnostics
  • Computational Fluid Dynamics in Cardiology

Background:

  • Quantitative flow ratio (μFR) derived from coronary angiography images is validated for assessing fractional flow reserve (FFR).
  • Semi-automated computed μFR (CT-μFR) from coronary computed tomography angiography (CCTA) shows promise in identifying flow-limiting lesions.
  • Need for accurate, non-invasive methods to assess coronary stenosis severity prior to invasive procedures.

Purpose of the Study:

  • To evaluate the diagnostic accuracy of a fully automatic, AI-powered method for CCTA reconstruction and CT-μFR computation.
  • To compare the performance of AI-driven CT-μFR against invasive cath lab-derived FFR and μFR as the reference standard.

Main Methods:

  • Post-hoc analysis of the prospective CAREER trial (NCT04665817) involving patients who underwent both CCTA and invasive coronary angiography with FFR.
  • Hemodynamically significant stenosis defined by FFR ≤ 0.80 or μFR ≤ 0.80.
  • Artificial intelligence was used for fully automatic CCTA reconstruction and CT-μFR calculation.

Main Results:

  • Successful automatic CCTA reconstruction and CT-μFR computation in 657 vessels from 242 patients.
  • CT-μFR demonstrated good correlation (r=0.62) and agreement with cath lab physiology.
  • Patient-level diagnostic accuracy for identifying significant stenosis was 83.0%, with sensitivity of 84.2% and specificity of 81.9%. Average analysis time was 1.60 minutes per patient.

Conclusions:

  • The fully automatic AI-powered CT-μFR method is highly feasible for clinical application.
  • This approach demonstrates good diagnostic performance in identifying hemodynamically significant coronary stenosis.
  • AI-driven CT-μFR offers a promising non-invasive tool for pre-procedural patient assessment.