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Related Experiment Video

Updated: Aug 11, 2025

Author Spotlight: Improved Localization and Monitoring of Coronary Flow Reserve Using Modified PLAX View in Mice

Author Spotlight: Improved Localization and Monitoring of Coronary Flow Reserve Using Modified PLAX View in Mice

Published on: August 25, 2023

Coronary Flow Rate Adds Predictive Capability for FFR Assessment.

Jacob Miller¹, John White¹, Javad Hashemi¹

¹University of Louisville.

Research Square

|February 7, 2023

Summary

This summary is machine-generated.

A new risk assessment tool combining anatomical and coronary artery flow rate (CFR) data improves coronary artery stenosis evaluation. This approach enhances the accuracy of predicting fractional flow reserve (FFR) compared to anatomy-only models.

Keywords:

FFR clinical prediction models hemodynamics risk stratification

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Ultrasound Based Assessment of Coronary Artery Flow and Coronary Flow Reserve Using the Pressure Overload Model in Mice

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Last Updated: Aug 11, 2025

Author Spotlight: Improved Localization and Monitoring of Coronary Flow Reserve Using Modified PLAX View in Mice

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Published on: August 25, 2023

Evaluation of Coronary Flow Reserve After Myocardial Ischemia Reperfusion in Rats

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Published on: June 28, 2019

Ultrasound Based Assessment of Coronary Artery Flow and Coronary Flow Reserve Using the Pressure Overload Model in Mice

Ultrasound Based Assessment of Coronary Artery Flow and Coronary Flow Reserve Using the Pressure Overload Model in Mice

Published on: April 13, 2015

Area of Science:

Cardiovascular medicine
Medical imaging analysis
Biomedical engineering

Background:

Current methods for assessing coronary artery disease (CAD) often rely on invasive procedures like fractional flow reserve (FFR).
Anatomical parameters alone, such as percent diameter stenosis (%DS), provide insufficient accuracy in predictive models for FFR.
There is a need for non-invasive tools to accurately stratify coronary artery stenosis risk.

Conclusions:

Combining anatomical (%DS) and physiological (CFR) parameters significantly improves the accuracy of statistical models for predicting FFR.
Integrating CFR into risk assessment models offers a more robust non-invasive approach to evaluating coronary artery stenosis.
This enhanced approach could potentially reduce the need for invasive FFR procedures in managing CAD.