Automating Fractional Flow Reserve (FFR) calculation from CT scans: A rapid workflow using unsupervised learning and computational fluid dynamics.
Neeraj Kavan ChakshuJason M CarsonIgor SazonovPerumal NithiarasuPublished in: International journal for numerical methods in biomedical engineering (2021)
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. This article is protected by copyright. All rights reserved.
Keyphrases
- computed tomography
- dual energy
- contrast enhanced
- coronary artery
- coronary artery disease
- image quality
- machine learning
- randomized controlled trial
- healthcare
- positron emission tomography
- magnetic resonance imaging
- public health
- magnetic resonance
- loop mediated isothermal amplification
- electronic health record
- aortic stenosis
- quality improvement
- ultrasound guided
- percutaneous coronary intervention
- pet ct