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Estimating the irreversible pressure drop across a stenosis by quantifying turbulence production using 4D Flow MRI

The pressure drop across a stenotic vessel is an important parameter in medicine, providing a commonly used and intuitive metric for evaluating the severity of the stenosis. However, non-invasive estimation of the pressure drop under pathological conditions has remained difficult. This study demonst...

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Autores principales: Ha, Hojin, Lantz, Jonas, Ziegler, Magnus, Casas, Belen, Karlsson, Matts, Dyverfeldt, Petter, Ebbers, Tino
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5397859/
https://www.ncbi.nlm.nih.gov/pubmed/28425452
http://dx.doi.org/10.1038/srep46618
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author Ha, Hojin
Lantz, Jonas
Ziegler, Magnus
Casas, Belen
Karlsson, Matts
Dyverfeldt, Petter
Ebbers, Tino
author_facet Ha, Hojin
Lantz, Jonas
Ziegler, Magnus
Casas, Belen
Karlsson, Matts
Dyverfeldt, Petter
Ebbers, Tino
author_sort Ha, Hojin
collection PubMed
description The pressure drop across a stenotic vessel is an important parameter in medicine, providing a commonly used and intuitive metric for evaluating the severity of the stenosis. However, non-invasive estimation of the pressure drop under pathological conditions has remained difficult. This study demonstrates a novel method to quantify the irreversible pressure drop across a stenosis using 4D Flow MRI by calculating the total turbulence production of the flow. Simulation MRI acquisitions showed that the energy lost to turbulence production can be accurately quantified with 4D Flow MRI within a range of practical spatial resolutions (1–3 mm; regression slope = 0.91, R(2) = 0.96). The quantification of the turbulence production was not substantially influenced by the signal-to-noise ratio (SNR), resulting in less than 2% mean bias at SNR > 10. Pressure drop estimation based on turbulence production robustly predicted the irreversible pressure drop, regardless of the stenosis severity and post-stenosis dilatation (regression slope = 0.956, R(2) = 0.96). In vitro validation of the technique in a 75% stenosis channel confirmed that pressure drop prediction based on the turbulence production agreed with the measured pressure drop (regression slope = 1.15, R(2) = 0.999, Bland-Altman agreement = 0.75 ± 3.93 mmHg).
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spelling pubmed-53978592017-04-21 Estimating the irreversible pressure drop across a stenosis by quantifying turbulence production using 4D Flow MRI Ha, Hojin Lantz, Jonas Ziegler, Magnus Casas, Belen Karlsson, Matts Dyverfeldt, Petter Ebbers, Tino Sci Rep Article The pressure drop across a stenotic vessel is an important parameter in medicine, providing a commonly used and intuitive metric for evaluating the severity of the stenosis. However, non-invasive estimation of the pressure drop under pathological conditions has remained difficult. This study demonstrates a novel method to quantify the irreversible pressure drop across a stenosis using 4D Flow MRI by calculating the total turbulence production of the flow. Simulation MRI acquisitions showed that the energy lost to turbulence production can be accurately quantified with 4D Flow MRI within a range of practical spatial resolutions (1–3 mm; regression slope = 0.91, R(2) = 0.96). The quantification of the turbulence production was not substantially influenced by the signal-to-noise ratio (SNR), resulting in less than 2% mean bias at SNR > 10. Pressure drop estimation based on turbulence production robustly predicted the irreversible pressure drop, regardless of the stenosis severity and post-stenosis dilatation (regression slope = 0.956, R(2) = 0.96). In vitro validation of the technique in a 75% stenosis channel confirmed that pressure drop prediction based on the turbulence production agreed with the measured pressure drop (regression slope = 1.15, R(2) = 0.999, Bland-Altman agreement = 0.75 ± 3.93 mmHg). Nature Publishing Group 2017-04-20 /pmc/articles/PMC5397859/ /pubmed/28425452 http://dx.doi.org/10.1038/srep46618 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Ha, Hojin
Lantz, Jonas
Ziegler, Magnus
Casas, Belen
Karlsson, Matts
Dyverfeldt, Petter
Ebbers, Tino
Estimating the irreversible pressure drop across a stenosis by quantifying turbulence production using 4D Flow MRI
title Estimating the irreversible pressure drop across a stenosis by quantifying turbulence production using 4D Flow MRI
title_full Estimating the irreversible pressure drop across a stenosis by quantifying turbulence production using 4D Flow MRI
title_fullStr Estimating the irreversible pressure drop across a stenosis by quantifying turbulence production using 4D Flow MRI
title_full_unstemmed Estimating the irreversible pressure drop across a stenosis by quantifying turbulence production using 4D Flow MRI
title_short Estimating the irreversible pressure drop across a stenosis by quantifying turbulence production using 4D Flow MRI
title_sort estimating the irreversible pressure drop across a stenosis by quantifying turbulence production using 4d flow mri
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5397859/
https://www.ncbi.nlm.nih.gov/pubmed/28425452
http://dx.doi.org/10.1038/srep46618
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