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Enabling 3D-Liver Perfusion Mapping from MR-DCE Imaging Using Distributed Computing

An MR acquisition protocol and a processing method using distributed computing on the European Grid Infrastructure (EGI) to allow 3D liver perfusion parametric mapping after Magnetic Resonance Dynamic Contrast Enhanced (MR-DCE) imaging are presented. Seven patients (one healthy control and six with...

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Autores principales: Leporq, Benjamin, Camarasu-Pop, Sorina, Davila-Serrano, Eduardo E., Pilleul, Frank, Beuf, Olivier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4782628/
https://www.ncbi.nlm.nih.gov/pubmed/27006915
http://dx.doi.org/10.1155/2013/471682
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author Leporq, Benjamin
Camarasu-Pop, Sorina
Davila-Serrano, Eduardo E.
Pilleul, Frank
Beuf, Olivier
author_facet Leporq, Benjamin
Camarasu-Pop, Sorina
Davila-Serrano, Eduardo E.
Pilleul, Frank
Beuf, Olivier
author_sort Leporq, Benjamin
collection PubMed
description An MR acquisition protocol and a processing method using distributed computing on the European Grid Infrastructure (EGI) to allow 3D liver perfusion parametric mapping after Magnetic Resonance Dynamic Contrast Enhanced (MR-DCE) imaging are presented. Seven patients (one healthy control and six with chronic liver diseases) were prospectively enrolled after liver biopsy. MR-dynamic acquisition was continuously performed in free-breathing during two minutes after simultaneous intravascular contrast agent (MS-325 blood pool agent) injection. Hepatic capillary system was modeled by a 3-parameters one-compartment pharmacokinetic model. The processing step was parallelized and executed on the EGI. It was modeled and implemented as a grid workflow using the Gwendia language and the MOTEUR workflow engine. Results showed good reproducibility in repeated processing on the grid. The results obtained from the grid were well correlated with ROI-based reference method ran locally on a personal computer. The speed-up range was 71 to 242 with an average value of 126. In conclusion, distributed computing applied to perfusion mapping brings significant speed-up to quantification step to be used for further clinical studies in a research context. Accuracy would be improved with higher image SNR accessible on the latest 3T MR systems available today.
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spelling pubmed-47826282016-03-22 Enabling 3D-Liver Perfusion Mapping from MR-DCE Imaging Using Distributed Computing Leporq, Benjamin Camarasu-Pop, Sorina Davila-Serrano, Eduardo E. Pilleul, Frank Beuf, Olivier J Med Eng Research Article An MR acquisition protocol and a processing method using distributed computing on the European Grid Infrastructure (EGI) to allow 3D liver perfusion parametric mapping after Magnetic Resonance Dynamic Contrast Enhanced (MR-DCE) imaging are presented. Seven patients (one healthy control and six with chronic liver diseases) were prospectively enrolled after liver biopsy. MR-dynamic acquisition was continuously performed in free-breathing during two minutes after simultaneous intravascular contrast agent (MS-325 blood pool agent) injection. Hepatic capillary system was modeled by a 3-parameters one-compartment pharmacokinetic model. The processing step was parallelized and executed on the EGI. It was modeled and implemented as a grid workflow using the Gwendia language and the MOTEUR workflow engine. Results showed good reproducibility in repeated processing on the grid. The results obtained from the grid were well correlated with ROI-based reference method ran locally on a personal computer. The speed-up range was 71 to 242 with an average value of 126. In conclusion, distributed computing applied to perfusion mapping brings significant speed-up to quantification step to be used for further clinical studies in a research context. Accuracy would be improved with higher image SNR accessible on the latest 3T MR systems available today. Hindawi Publishing Corporation 2013 2013-02-26 /pmc/articles/PMC4782628/ /pubmed/27006915 http://dx.doi.org/10.1155/2013/471682 Text en Copyright © 2013 Benjamin Leporq et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Leporq, Benjamin
Camarasu-Pop, Sorina
Davila-Serrano, Eduardo E.
Pilleul, Frank
Beuf, Olivier
Enabling 3D-Liver Perfusion Mapping from MR-DCE Imaging Using Distributed Computing
title Enabling 3D-Liver Perfusion Mapping from MR-DCE Imaging Using Distributed Computing
title_full Enabling 3D-Liver Perfusion Mapping from MR-DCE Imaging Using Distributed Computing
title_fullStr Enabling 3D-Liver Perfusion Mapping from MR-DCE Imaging Using Distributed Computing
title_full_unstemmed Enabling 3D-Liver Perfusion Mapping from MR-DCE Imaging Using Distributed Computing
title_short Enabling 3D-Liver Perfusion Mapping from MR-DCE Imaging Using Distributed Computing
title_sort enabling 3d-liver perfusion mapping from mr-dce imaging using distributed computing
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4782628/
https://www.ncbi.nlm.nih.gov/pubmed/27006915
http://dx.doi.org/10.1155/2013/471682
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