Fractional flow reserve derived from coronary computed tomography angiography (FFR- CT) has emerged as a non-invasive modality for the functional assessment of coronary artery disease. By using computational fluid dynamics, particularly in its most extensively validated off-site implementation, FFR-CT enables lesion-specific estimation of pressure gradients across coronary stenoses without the need for invasive catheterization. This narrative review summarizes the technical foundations of FFR-CT as well as the evidence demonstrating that FFR-CT enhances the diagnostic accuracy of coronary CT angiography alone by improving specificity for hemodynamically significant stenoses when compared with invasive fractional flow reserve. Beyond diagnosis, FFR-CT provides incremental prognostic information, supporting risk stratification and guiding revascularization deci- sions. Suggestions for clinical implementation of FFR-CT and guidance on interpreting results within the appropriate clinical context are provided. Despite these advantages, limi- tations remain, including dependence on image quality, reduced performance in heavily calcified vessels, assumptions regarding hyperemic flow conditions, and limited validation in certain populations. While computational fluid dynamics-based FFR-CT remains the most commonly adopted approach in clinical settings, machine learning-based on-site FFR-CT is rapidly evolving and is expected to become a reliable alternative. As technical refinements continue, FFR-CT is poised to play an expanding role in precision-guided management of coronary artery disease.

FFR-CT: Technical Advances and Implementation in Clinical Practice / K. Stankowski, A.P.. - In: JOURNAL OF IMAGING. - ISSN 2313-433X. - 12:5(2026), pp. 202.1-202.25. [10.3390/jimaging12050202]

FFR-CT: Technical Advances and Implementation in Clinical Practice

A. Pellizzon
Secondo
;
L. Signorelli;G. Pontone
Ultimo
2026

Abstract

Fractional flow reserve derived from coronary computed tomography angiography (FFR- CT) has emerged as a non-invasive modality for the functional assessment of coronary artery disease. By using computational fluid dynamics, particularly in its most extensively validated off-site implementation, FFR-CT enables lesion-specific estimation of pressure gradients across coronary stenoses without the need for invasive catheterization. This narrative review summarizes the technical foundations of FFR-CT as well as the evidence demonstrating that FFR-CT enhances the diagnostic accuracy of coronary CT angiography alone by improving specificity for hemodynamically significant stenoses when compared with invasive fractional flow reserve. Beyond diagnosis, FFR-CT provides incremental prognostic information, supporting risk stratification and guiding revascularization deci- sions. Suggestions for clinical implementation of FFR-CT and guidance on interpreting results within the appropriate clinical context are provided. Despite these advantages, limi- tations remain, including dependence on image quality, reduced performance in heavily calcified vessels, assumptions regarding hyperemic flow conditions, and limited validation in certain populations. While computational fluid dynamics-based FFR-CT remains the most commonly adopted approach in clinical settings, machine learning-based on-site FFR-CT is rapidly evolving and is expected to become a reliable alternative. As technical refinements continue, FFR-CT is poised to play an expanding role in precision-guided management of coronary artery disease.
cardiac computed tomography; chronic coronary syndrome; computational fluid dynamics; coronary artery disease; fractional flow reserve; machine learning
Settore MEDS-07/B - Malattie dell'apparato cardiovascolare
2026
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1259903
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