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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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Automating fractional flow reserve (FFR) calculation from CT scans: A rapid workflow using unsupervised learning and

Neeraj Kavan Chakshu¹, Jason M Carson¹, Igor Sazonov¹

¹Biomedical Engineering Group, Zienkiewicz Centre for Computational Engineering, Faculty of Science and Engineering, Swansea University, Swansea, UK.

International Journal for Numerical Methods in Biomedical Engineering

|December 5, 2021

Summary

A new unsupervised learning method rapidly calculates coronary computed tomography angiography-derived fractional flow reserve (cFFR) from CT scans. This automation reduces the labor of non-invasive FFR assessment, improving patient outcomes and cost-effectiveness.

Keywords:

automation computational fluid dynamics computer vision coronary system fractional flow reserve passive digital twin vessel segmentation

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Area of Science:

Cardiovascular imaging and diagnostics
Computational fluid dynamics
Artificial intelligence in medicine

Background:

Fractional flow reserve (FFR) is crucial for assessing coronary artery disease severity.
FFR-guided stenting improves outcomes and reduces costs compared to anatomical assessment alone.
Current non-invasive methods like coronary CT angiography-derived FFR (cFFR) are accurate but computationally intensive.

Purpose of the Study:

To develop a rapid, automated method for calculating cFFR from CT angiography.
To overcome the limitations of manual, labor-intensive cFFR computation.
To enable wider clinical adoption of non-invasive FFR assessment.

Main Methods:

Utilized unsupervised machine learning algorithms.
Developed a novel approach for automatic cFFR calculation from coronary CT scans.
Integrated image analysis and computational mechanics without manual intervention.

Main Results:

Successfully automated the cFFR calculation process.
Significantly reduced the time and expertise required for cFFR analysis.
Demonstrated a rapid and efficient method for non-invasive FFR assessment.

Conclusions:

The presented unsupervised learning method offers a fast and automated solution for cFFR calculation.
This approach has the potential to make non-invasive FFR assessment more accessible and cost-effective.
Automation of cFFR can facilitate broader clinical implementation of FFR-guided strategies.