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Related Experiment Video

Updated: Jun 9, 2025

Author Spotlight: Improved Localization and Monitoring of Coronary Flow Reserve Using Modified PLAX View in Mice
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CT coronary fractional flow reserve based on artificial intelligence using different software: a repeatability study.

Jing Li1, Zhenxing Yang1, Zhenting Sun1

  • 1Department of Radiology, the Affiliated Hospital of Inner Mongolia Medical University, No.1 Tongdao North Street, Hohhot, Inner Mongolia, 010050, China.

BMC Medical Imaging
|October 25, 2024
PubMed
Summary
This summary is machine-generated.

CT-FFR software shows inconsistencies in measuring coronary blood flow reserve fractions, impacting ischemia assessment in heart disease patients. Factors like vessel shifting length and image quality influence these discrepancies.

Keywords:
Coronary flow reserve fractionDifferenceInfluencing factorsReliability

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Computational Physiology

Background:

  • Accurate assessment of coronary blood flow reserve fractions is crucial for diagnosing ischemia in coronary heart disease.
  • Computed tomography-derived fractional flow reserve (CT-FFR) offers a non-invasive method for this assessment.
  • Understanding the consistency and reliability of different CT-FFR software is essential for clinical application.

Purpose of the Study:

  • To evaluate the consistency and reliability of different CT-FFR software.
  • To identify factors influencing CT-FFR measurement discrepancies.
  • To enhance the accuracy of CT-FFR for ischemia assessment.

Main Methods:

  • A prospective study included 103 participants assessed with coronary computed tomography angiography (CCTA).
  • CT-FFR calculations were performed using K software and S software.
  • Consistency was analyzed using paired-sample t-tests and Bland-Altman plots; influence factors were assessed via ROC curve analysis.

Main Results:

  • Significant differences in CT-FFR measurements were observed between K and S software (P < 0.05).
  • 6% of measurements fell outside the 95% consistency level in Bland-Altman plots.
  • Heart rate variability, vessel shifting length, subjective image quality, and lumen diameter significantly impacted measurement discrepancies (P < 0.05).

Conclusions:

  • CT-FFR measurements exhibit variability across different software manufacturers, potentially leading to diagnostic misclassification.
  • Vessel shifting length, subjective image quality, heart rate variability (HRv), and lumen diameter are key factors affecting CT-FFR measurement stability.