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CT derived fractional flow reserve: Part 1 – Comprehensive review of methodologies

  • Kashif Shaikh
  • , Patricia Rodriguez Lozano
  • , Sotirios Evangelou
  • , En-Haw Wu
  • , Nick S. Nurmohamed
  • , Nidhi Madan
  • , Dhiran Verghese
  • , Chandana Shekar
  • , Anam Waheed
  • , Saira Siddiqui
  • , M. rton Kolossváry
  • , Shone Almeida
  • , Tyler Coombes
  • , Dominika Suchá
  • , Siddharth J. Trivedi
  • , the SCCT FiRST (Fellow and Resident Leaders of SCCT) Committee
  • University of Tennessee Health Science Center
  • University of Virginia
  • European Interbalkan Medical Center
  • Oregon Health and Science University
  • Vrije Universiteit Amsterdam
  • University of Amsterdam
  • George Washington University
  • UnityPoint Health
  • NCH
  • Portneuf Medical Center
  • Washington Hospital Center
  • Morristown Medical Center
  • Thomas Jefferson University
  • Gottsegen National Cardiovascular Center
  • Óbuda University
  • University of South Florida
  • Utrecht University
  • Harvard University
  • Fiona Stanley Hospital
  • Curtin University

Research output: Contribution to journalReview articleAcademicpeer-review

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Abstract

Advancements in cardiac computed tomography angiography (CCTA) have enabled the extraction of physiological data from an anatomy-based imaging modality. This review outlines the key methodologies for deriving fractional flow reserve (FFR) from CCTA, with a focus on two primary methods: 1) computational fluid dynamics-based FFR (CT-FFR) and 2) plaque-derived ischemia assessment using artificial intelligence and quantitative plaque metrics. These techniques have expanded the role of CCTA beyond anatomical assessment, allowing for concurrent evaluation of coronary physiology without the need for invasive testing. This review provides an overview of the principles, workflows, and limitations of each technique and aims to inform on the current state and future direction of non-invasive coronary physiology assessment.
Original languageEnglish
Pages (from-to)390-396
Number of pages7
JournalJournal of cardiovascular computed tomography
Volume19
Issue number4
Early online date2025
DOIs
Publication statusPublished - 1 Jul 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Computational fractional flow reserve
  • Coronary artery disease
  • Coronary computed tomography angiography

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