An automated and time-efficient framework for simulation of coronary blood flow under steady and pulsatile conditions.

Computational fluid dynamics Computed tomography Coronary artery FFR FFR-CT Fractional flow reserve

Journal

Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513

Informations de publication

Date de publication:
06 Sep 2024
Historique:
received: 16 05 2024
revised: 01 08 2024
accepted: 05 09 2024
medline: 14 9 2024
pubmed: 14 9 2024
entrez: 13 9 2024
Statut: aheadofprint

Résumé

Invasive fractional flow reserve (FFR) measurement is the gold standard method for coronary artery disease (CAD) diagnosis. FFR-CT exploits computational fluid dynamics (CFD) for non-invasive evaluation of FFR, simulating coronary flow in virtual geometries reconstructed from computed tomography (CT), but suffers from cost-intensive computing process and uncertainties in the definition of patient specific boundary conditions (BCs). In this work, we investigated the use of time-averaged steady BCs, compared to pulsatile to reduce the computational time and deployed a self-adjusting method for the tuning of BCs to patient-specific clinical data. 133 coronary arteries were reconstructed form CT images of patients suffering from CAD. For each vessel, invasive FFR was measured. After segmentation, the geometries were prepared for CFD simulation by clipping the outlets and discretizing into tetrahedral mesh. Steady BCs were defined in two steps: (i) rest BCs were extrapolated from clinical and image-derived data; (ii) hyperemic BCs were computed from resting conditions. Flow rate was iteratively adjusted during the simulation, until patient's aortic pressure was matched. Pulsatile BCs were defined exploiting the convergence values of steady BCs. After CFD simulation, lesion-specific hemodynamic indexes were computed and compared between group of patients for which surgery was indicated and not. The whole pipeline was implemented as a straightforward process, in which each single step is performed automatically. Steady and pulsatile FFR-CT yielded a strong correlation (r = 0.988, p < 0.001) and correlated with invasive FFR (r = 0.797, p < 0.001). The per-point difference between the pressure and FFR-CT field predicted by the two methods was below 1 % and 2 %, respectively. Both approaches exhibited a good diagnostic performance: accuracy was 0.860 and 0.864, the AUC was 0.923 and 0.912, for steady and pulsatile case, respectively. The computational time required by steady BCs CFD was approximatively 30-folds lower than pulsatile case. This work shows the feasibility of using steady BCs CFD for computing the FFR-CT in coronary arteries, as well as its computational and diagnostic performance within a fully automated pipeline.

Sections du résumé

BACKGROUND AND OBJECTIVE OBJECTIVE
Invasive fractional flow reserve (FFR) measurement is the gold standard method for coronary artery disease (CAD) diagnosis. FFR-CT exploits computational fluid dynamics (CFD) for non-invasive evaluation of FFR, simulating coronary flow in virtual geometries reconstructed from computed tomography (CT), but suffers from cost-intensive computing process and uncertainties in the definition of patient specific boundary conditions (BCs). In this work, we investigated the use of time-averaged steady BCs, compared to pulsatile to reduce the computational time and deployed a self-adjusting method for the tuning of BCs to patient-specific clinical data.
METHODS METHODS
133 coronary arteries were reconstructed form CT images of patients suffering from CAD. For each vessel, invasive FFR was measured. After segmentation, the geometries were prepared for CFD simulation by clipping the outlets and discretizing into tetrahedral mesh. Steady BCs were defined in two steps: (i) rest BCs were extrapolated from clinical and image-derived data; (ii) hyperemic BCs were computed from resting conditions. Flow rate was iteratively adjusted during the simulation, until patient's aortic pressure was matched. Pulsatile BCs were defined exploiting the convergence values of steady BCs. After CFD simulation, lesion-specific hemodynamic indexes were computed and compared between group of patients for which surgery was indicated and not. The whole pipeline was implemented as a straightforward process, in which each single step is performed automatically.
RESULTS RESULTS
Steady and pulsatile FFR-CT yielded a strong correlation (r = 0.988, p < 0.001) and correlated with invasive FFR (r = 0.797, p < 0.001). The per-point difference between the pressure and FFR-CT field predicted by the two methods was below 1 % and 2 %, respectively. Both approaches exhibited a good diagnostic performance: accuracy was 0.860 and 0.864, the AUC was 0.923 and 0.912, for steady and pulsatile case, respectively. The computational time required by steady BCs CFD was approximatively 30-folds lower than pulsatile case.
CONCLUSIONS CONCLUSIONS
This work shows the feasibility of using steady BCs CFD for computing the FFR-CT in coronary arteries, as well as its computational and diagnostic performance within a fully automated pipeline.

Identifiants

pubmed: 39270532
pii: S0169-2607(24)00408-5
doi: 10.1016/j.cmpb.2024.108415
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

108415

Informations de copyright

Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest All authors disclose that they have no conflict of interest, no financial arrangement with products figuring in the submitted manuscript or with a company making a competing product. All those who fulfil the criteria for authorship have been included as authors and each author contributed significantly to the submitted work.

Auteurs

Guido Nannini (G)

Department of Electronics Information and Bioengineering, Politecnico di Milano, Milan, Italy. Electronic address: guido.nannini@polimi.it.

Simone Saitta (S)

Department of Electronics Information and Bioengineering, Politecnico di Milano, Milan, Italy.

Luca Mariani (L)

Department of Electronics Information and Bioengineering, Politecnico di Milano, Milan, Italy.

Riccardo Maragna (R)

Department of Perioperative Cardiology and Cardiovascular Imaging D, Centro Cardiologico Monzino IRCCS, Milan, Italy.

Andrea Baggiano (A)

Department of Perioperative Cardiology and Cardiovascular Imaging D, Centro Cardiologico Monzino IRCCS, Milan, Italy; Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy.

Saima Mushtaq (S)

Department of Perioperative Cardiology and Cardiovascular Imaging D, Centro Cardiologico Monzino IRCCS, Milan, Italy.

Gianluca Pontone (G)

Department of Perioperative Cardiology and Cardiovascular Imaging D, Centro Cardiologico Monzino IRCCS, Milan, Italy; Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy.

Alberto Redaelli (A)

Department of Electronics Information and Bioengineering, Politecnico di Milano, Milan, Italy.

Classifications MeSH