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Simulation of phase contrast angiography for renal arterial models

BACKGROUND: With the development of versatile magnetic resonance acquisition techniques there arises a need for more advanced imaging simulation tools to enable adequate image appearance prediction, measurement sequence design and testing thereof. Recently, there is a growing interest in phase contr...

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Autores principales: Klepaczko, Artur, Szczypiński, Piotr, Strzelecki, Michał, Stefańczyk, Ludomir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5902949/
https://www.ncbi.nlm.nih.gov/pubmed/29661193
http://dx.doi.org/10.1186/s12938-018-0471-y
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author Klepaczko, Artur
Szczypiński, Piotr
Strzelecki, Michał
Stefańczyk, Ludomir
author_facet Klepaczko, Artur
Szczypiński, Piotr
Strzelecki, Michał
Stefańczyk, Ludomir
author_sort Klepaczko, Artur
collection PubMed
description BACKGROUND: With the development of versatile magnetic resonance acquisition techniques there arises a need for more advanced imaging simulation tools to enable adequate image appearance prediction, measurement sequence design and testing thereof. Recently, there is a growing interest in phase contrast angiography (PCA) sequence due to the capabilities of blood flow quantification that it offers. Moreover, as it is a non-contrast enhanced protocol, it has become an attractive option in areas, where usage of invasive contrast agents is not indifferent for the imaged tissue. Monitoring of the kidney function is an example of such an application. RESULTS: We present a computer framework for simulation of the PCA protocol, both conventional and accelerated with echo-planar imaging (EPI) readout, and its application to the numerical models of kidney vasculatures. Eight patient-specific renal arterial trees were reconstructed following vessel segmentation in real computed tomography angiograms. In addition, a synthetic model was designed using a vascular tree growth simulation algorithm. The results embrace a series of synthetic PCA images of the renal arterial trees giving insight into the image formation and quantification of kidney hemodynamics. CONCLUSIONS: The designed simulation framework enables quantification of the PCA measurement error in relation to ground-truth flow velocity data. The mean velocity measurement error for the reconstructed renal arterial trees range from 1.5 to 12.8% of the aliasing velocity value, depending on image resolution and flip angle. No statistically significant difference was observed between measurements obtained using EPI with a number of echos (NETL) = 4 and conventional PCA. In case of higher NETL factors peak velocity values can be underestimated up to 34%.
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spelling pubmed-59029492018-04-23 Simulation of phase contrast angiography for renal arterial models Klepaczko, Artur Szczypiński, Piotr Strzelecki, Michał Stefańczyk, Ludomir Biomed Eng Online Research BACKGROUND: With the development of versatile magnetic resonance acquisition techniques there arises a need for more advanced imaging simulation tools to enable adequate image appearance prediction, measurement sequence design and testing thereof. Recently, there is a growing interest in phase contrast angiography (PCA) sequence due to the capabilities of blood flow quantification that it offers. Moreover, as it is a non-contrast enhanced protocol, it has become an attractive option in areas, where usage of invasive contrast agents is not indifferent for the imaged tissue. Monitoring of the kidney function is an example of such an application. RESULTS: We present a computer framework for simulation of the PCA protocol, both conventional and accelerated with echo-planar imaging (EPI) readout, and its application to the numerical models of kidney vasculatures. Eight patient-specific renal arterial trees were reconstructed following vessel segmentation in real computed tomography angiograms. In addition, a synthetic model was designed using a vascular tree growth simulation algorithm. The results embrace a series of synthetic PCA images of the renal arterial trees giving insight into the image formation and quantification of kidney hemodynamics. CONCLUSIONS: The designed simulation framework enables quantification of the PCA measurement error in relation to ground-truth flow velocity data. The mean velocity measurement error for the reconstructed renal arterial trees range from 1.5 to 12.8% of the aliasing velocity value, depending on image resolution and flip angle. No statistically significant difference was observed between measurements obtained using EPI with a number of echos (NETL) = 4 and conventional PCA. In case of higher NETL factors peak velocity values can be underestimated up to 34%. BioMed Central 2018-04-16 /pmc/articles/PMC5902949/ /pubmed/29661193 http://dx.doi.org/10.1186/s12938-018-0471-y Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Klepaczko, Artur
Szczypiński, Piotr
Strzelecki, Michał
Stefańczyk, Ludomir
Simulation of phase contrast angiography for renal arterial models
title Simulation of phase contrast angiography for renal arterial models
title_full Simulation of phase contrast angiography for renal arterial models
title_fullStr Simulation of phase contrast angiography for renal arterial models
title_full_unstemmed Simulation of phase contrast angiography for renal arterial models
title_short Simulation of phase contrast angiography for renal arterial models
title_sort simulation of phase contrast angiography for renal arterial models
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5902949/
https://www.ncbi.nlm.nih.gov/pubmed/29661193
http://dx.doi.org/10.1186/s12938-018-0471-y
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