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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, 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, 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
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: Faculty of Science and Engineering
Funders: Global Challenges Research Fund. Grant Number: RB1819APM003SWANKARU; Medical Research Council. Grant Number: MR/S004076/1; College of Engineering, Swansea University
Issue: 3

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