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

Neeraj Kavan Chakshu, Jason Carson, Igor Sazonov Orcid Logo, Perumal Nithiarasu Orcid Logo

International Journal for Numerical Methods in Biomedical Engineering, Volume: 38, Issue: 3

Swansea University Authors: Neeraj Kavan Chakshu, Jason Carson, Igor Sazonov Orcid Logo, Perumal Nithiarasu Orcid Logo

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DOI (Published version): 10.1002/cnm.3559

Abstract

Fractional flow reserve (FFR) provides the functional relevance of coronary atheroma. The FFR-guided strategy has been shown to reduce unnecessary stenting, improve overall health outcome, and to be cost-saving. The non-invasive, coronary Computerised Tomography (CT) angiography-derived FFR (cFFR) i...

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Published in: International Journal for Numerical Methods in Biomedical Engineering
ISSN: 2040-7939 2040-7947
Published: Wiley 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa58926
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Abstract: Fractional flow reserve (FFR) provides the functional relevance of coronary atheroma. The FFR-guided strategy has been shown to reduce unnecessary stenting, improve overall health outcome, and to be cost-saving. The non-invasive, coronary Computerised Tomography (CT) angiography-derived FFR (cFFR) is an emerging method in reducing invasive catheter based measurements. This CFD-based method is laborious as it requires expertise in multidisciplinary analysis of combining image analysis and computational mechanics. In this work, we present a rapid method, powered by unsupervised learning, to automatically calculate cFFR from CT scans without manual intervention.
Keywords: Fractional Flow Reserve, Vessel Segmentation, Passive digital twin, CFD, Coronary system, Computervision, Automation
College: College of Engineering
Funders: Global Challenges Research Fund. Grant Number: RB1819APM003SWANKARU; Medical Research Council. Grant Number: MR/S004076/1; College of Engineering, Swansea University
Issue: 3