4D Flow MRI hemodynamic benchmarking of surgical bioprosthetic valves.


Journal

Magnetic resonance imaging
ISSN: 1873-5894
Titre abrégé: Magn Reson Imaging
Pays: Netherlands
ID NLM: 8214883

Informations de publication

Date de publication:
05 2020
Historique:
received: 15 10 2019
revised: 23 12 2019
accepted: 19 01 2020
pubmed: 26 1 2020
medline: 26 11 2020
entrez: 26 1 2020
Statut: ppublish

Résumé

We exploited 4-dimensional flow magnetic resonance imaging (4D Flow), combined with a standardized in vitro setting, to establish a comprehensive benchmark for the systematic hemodynamic comparison of surgical aortic bioprosthetic valves (BPVs). 4D Flow analysis was performed on two small sizes of three commercialized pericardial BPVs (Trifecta™ GT, Carpentier-Edwards PERIMOUNT Magna and Crown PRT®). Each BPV was tested over a clinically pertinent range of continuous flow rates within an in vitro MRI-compatible system, equipped with pressure transducers. In-house 4D Flow post-processing of the post-valvular velocity field included the quantification of BPV effective orifice area (EOA), transvalvular pressure gradients (TPG), kinetic energy and viscous energy dissipation. The 4D Flow technique effectively captured the 3-dimensional flow pattern of each device. Trifecta exhibited the lowest range of velocity and kinetic energy, maximized EOA (p < 0.0001) and minimized TPGs (p ≤ 0.015) if compared with Magna and Crown, these reporting minor EOA difference s (p ≥ 0.042) and similar TPGs (p ≥ 0.25). 4D Flow TPGs estimations strongly correlated against ground-truth data from pressure transducers; viscous energy dissipation proved to be inversely proportional to the fluid jet penetration. The proposed 4D Flow analysis pinpointed consistent hemodynamic differences among BPVs, highlighting the not negligible effect of device size on the fluidynamic outcomes. The efficacy of non-invasive 4D Flow MRI protocol could shed light on how standardize the comparison among devices in relation to their actual hemodynamic performances and improve current criteria for their selection.

Identifiants

pubmed: 31981709
pii: S0730-725X(19)30625-3
doi: 10.1016/j.mri.2020.01.006
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

18-29

Informations de copyright

Copyright © 2020 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest None of the authors have conflicts of interest to declare.

Auteurs

Francesco Sturla (F)

3D and Computer Simulation Laboratory, IRCCS Policlinico San Donato, San Donato Milanese, Italy. Electronic address: francesco.sturla@grupposandonato.it.

Filippo Piatti (F)

3D and Computer Simulation Laboratory, IRCCS Policlinico San Donato, San Donato Milanese, Italy.

Michal Jaworek (M)

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

Federico Lucherini (F)

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

Francesca R Pluchinotta (FR)

Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy; Multimodality Cardiac Imaging, IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy; Department of Pediatric and Adult Congenital Heart Disease, IRCCS Policlinico San Donato, San Donato Milanese, Italy.

Sergii V Siryk (SV)

CONCEPT Lab, Istituto Italiano di Tecnologia, Genova, Italy.

Daniel Giese (D)

Siemens Healthcare GmbH, Erlangen, Germany.

Riccardo Vismara (R)

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

Giordano Tasca (G)

Cardiac Surgery Unit, Heart Health Center, King Saud Medical City, Riyadh, Saudi Arabia.

Lorenzo Menicanti (L)

Department of Cardiovascular Disease, IRCCS Policlinico San Donato, San Donato Milanese, Italy.

Alberto Redaelli (A)

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

Massimo Lombardi (M)

Multimodality Cardiac Imaging, IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy.

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Classifications MeSH