Literature DB >> 28600860

Estimating the accuracy of a reduced-order model for the calculation of fractional flow reserve (FFR).

Etienne Boileau1, Sanjay Pant1, Carl Roobottom2, Igor Sazonov1, Jingjing Deng3, Xianghua Xie3, Perumal Nithiarasu1.   

Abstract

Image-based noninvasive fractional flow reserve (FFR) is an emergent approach to determine the functional relevance of coronary stenoses. The present work aimed to determine the feasibility of using a method based on coronary computed tomography angiography (CCTA) and reduced-order models (0D-1D) for the evaluation of coronary stenoses. The reduced-order methodology (cFFRRO ) was kept as simple as possible and did not include pressure drop or stenosis models. The geometry definition was incorporated into the physical model used to solve coronary flow and pressure. cFFRRO was assessed on a virtual cohort of 30 coronary artery stenoses in 25 vessels and compared with a standard approach based on 3D computational fluid dynamics (cFFR3D ). In this proof-of-concept study, we sought to investigate the influence of geometry and boundary conditions on the agreement between both methods. Performance on a per-vessel level showed a good correlation between both methods (Pearson's product-moment R=0.885, P<0.01), when using cFFR3D  as the reference standard. The 95% limits of agreement were -0.116 and 0.08, and the mean bias was -0.018 (SD =0.05). Our results suggest no appreciable difference between cFFRRO  and cFFR3D with respect to lesion length and/or aspect ratio. At a fixed aspect ratio, however, stenosis severity and shape appeared to be the most critical factors accounting for differences in both methods. Despite the assumptions inherent to the 1D formulation, asymmetry did not seem to affect the agreement. The choice of boundary conditions is critical in obtaining a functionally significant drop in pressure. Our initial data suggest that this approach may be part of a broader risk assessment strategy aimed at increasing the diagnostic yield of cardiac catheterisation for in-hospital evaluation of haemodynamically significant stenoses.
Copyright © 2017 John Wiley & Sons, Ltd.

Entities:  

Keywords:  boundary conditions; coronary stenosis severity; non-invasive fractional flow reserve; reduced-order model; shape and asymmetry

Mesh:

Year:  2017        PMID: 28600860     DOI: 10.1002/cnm.2908

Source DB:  PubMed          Journal:  Int J Numer Method Biomed Eng        ISSN: 2040-7939            Impact factor:   2.747


  16 in total

1.  Reduced order models for transstenotic pressure drop in the coronary arteries.

Authors:  Mehran Mirramezani; Scott Diamond; Harold Litt; Shawn C Shadden
Journal:  J Biomech Eng       Date:  2018-12-05       Impact factor: 2.097

2.  Diagnostic performance of virtual fractional flow reserve derived from routine coronary angiography using segmentation free reduced order (1-dimensional) flow modelling.

Authors:  Kevin Mohee; Jonathan P Mynard; Gauravsingh Dhunnoo; Rhodri Davies; Perumal Nithiarasu; Julian P Halcox; Daniel R Obaid
Journal:  JRSM Cardiovasc Dis       Date:  2020-11-05

3.  Automated generation of 0D and 1D reduced-order models of patient-specific blood flow.

Authors:  Martin R Pfaller; Jonathan Pham; Aekaansh Verma; Luca Pegolotti; Nathan M Wilson; David W Parker; Weiguang Yang; Alison L Marsden
Journal:  Int J Numer Method Biomed Eng       Date:  2022-08-14       Impact factor: 2.648

4.  A 1D-3D Hybrid Model of Patient-Specific Coronary Hemodynamics.

Authors:  Noelia Grande Gutiérrez; Talid Sinno; Scott L Diamond
Journal:  Cardiovasc Eng Technol       Date:  2021-09-30       Impact factor: 2.305

5.  The effects of clinically-derived parametric data uncertainty in patient-specific coronary simulations with deformable walls.

Authors:  Jongmin Seo; Daniele E Schiavazzi; Andrew M Kahn; Alison L Marsden
Journal:  Int J Numer Method Biomed Eng       Date:  2020-06-25       Impact factor: 2.747

6.  Comparison of 1D and 3D Models for the Estimation of Fractional Flow Reserve.

Authors:  P J Blanco; C A Bulant; L O Müller; G D Maso Talou; C Guedes Bezerra; P A Lemos; R A Feijóo
Journal:  Sci Rep       Date:  2018-11-22       Impact factor: 4.379

7.  A framework for incorporating 3D hyperelastic vascular wall models in 1D blood flow simulations.

Authors:  Alberto Coccarelli; Jason M Carson; Ankush Aggarwal; Sanjay Pant
Journal:  Biomech Model Mechanobiol       Date:  2021-03-08

8.  Influence of ageing on human body blood flow and heat transfer: A detailed computational modelling study.

Authors:  Alberto Coccarelli; Hayder M Hasan; Jason Carson; Dimitris Parthimos; Perumal Nithiarasu
Journal:  Int J Numer Method Biomed Eng       Date:  2018-07-23       Impact factor: 2.747

9.  A semi-active human digital twin model for detecting severity of carotid stenoses from head vibration-A coupled computational mechanics and computer vision method.

Authors:  Neeraj Kavan Chakshu; Jason Carson; Igor Sazonov; Perumal Nithiarasu
Journal:  Int J Numer Method Biomed Eng       Date:  2019-02-20       Impact factor: 2.747

10.  Virtual FFR Quantified with a Generalized Flow Model Using Windkessel Boundary Conditions.

Authors:  Keltoum Chahour; Rajae Aboulaich; Abderrahmane Habbal; Nejib Zemzemi; Chérif Abdelkhirane
Journal:  Comput Math Methods Med       Date:  2020-02-21       Impact factor: 2.238

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