Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data

Abstract Blood flow metrics obtained with four-dimensional (4D) flow phase contrast (PC) magnetic resonance imaging (MRI) can be of great value in clinical and experimental cerebrovascular analysis. However, limitations in both quantitative and qualitative analyses can result from errors inherent to...

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Autores principales:	David R. Rutkowski, Alejandro Roldán-Alzate, Kevin M. Johnson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d295f749fc4349b59b3d4283af72621a
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spelling	oai:doaj.org-article:d295f749fc4349b59b3d4283af72621a2021-12-02T16:50:37ZEnhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data10.1038/s41598-021-89636-z2045-2322https://doaj.org/article/d295f749fc4349b59b3d4283af72621a2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89636-zhttps://doaj.org/toc/2045-2322Abstract Blood flow metrics obtained with four-dimensional (4D) flow phase contrast (PC) magnetic resonance imaging (MRI) can be of great value in clinical and experimental cerebrovascular analysis. However, limitations in both quantitative and qualitative analyses can result from errors inherent to PC MRI. One method that excels in creating low-error, physics-based, velocity fields is computational fluid dynamics (CFD). Augmentation of cerebral 4D flow MRI data with CFD-informed neural networks may provide a method to produce highly accurate physiological flow fields. In this preliminary study, the potential utility of such a method was demonstrated by using high resolution patient-specific CFD data to train a convolutional neural network, and then using the trained network to enhance MRI-derived velocity fields in cerebral blood vessel data sets. Through testing on simulated images, phantom data, and cerebrovascular 4D flow data from 20 patients, the trained network successfully de-noised flow images, decreased velocity error, and enhanced near-vessel-wall velocity quantification and visualization. Such image enhancement can improve experimental and clinical qualitative and quantitative cerebrovascular PC MRI analysis.David R. RutkowskiAlejandro Roldán-AlzateKevin M. JohnsonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q David R. Rutkowski Alejandro Roldán-Alzate Kevin M. Johnson Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data
description	Abstract Blood flow metrics obtained with four-dimensional (4D) flow phase contrast (PC) magnetic resonance imaging (MRI) can be of great value in clinical and experimental cerebrovascular analysis. However, limitations in both quantitative and qualitative analyses can result from errors inherent to PC MRI. One method that excels in creating low-error, physics-based, velocity fields is computational fluid dynamics (CFD). Augmentation of cerebral 4D flow MRI data with CFD-informed neural networks may provide a method to produce highly accurate physiological flow fields. In this preliminary study, the potential utility of such a method was demonstrated by using high resolution patient-specific CFD data to train a convolutional neural network, and then using the trained network to enhance MRI-derived velocity fields in cerebral blood vessel data sets. Through testing on simulated images, phantom data, and cerebrovascular 4D flow data from 20 patients, the trained network successfully de-noised flow images, decreased velocity error, and enhanced near-vessel-wall velocity quantification and visualization. Such image enhancement can improve experimental and clinical qualitative and quantitative cerebrovascular PC MRI analysis.
format	article
author	David R. Rutkowski Alejandro Roldán-Alzate Kevin M. Johnson
author_facet	David R. Rutkowski Alejandro Roldán-Alzate Kevin M. Johnson
author_sort	David R. Rutkowski
title	Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data
title_short	Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data
title_full	Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data
title_fullStr	Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data
title_full_unstemmed	Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data
title_sort	enhancement of cerebrovascular 4d flow mri velocity fields using machine learning and computational fluid dynamics simulation data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d295f749fc4349b59b3d4283af72621a
work_keys_str_mv	AT davidrrutkowski enhancementofcerebrovascular4dflowmrivelocityfieldsusingmachinelearningandcomputationalfluiddynamicssimulationdata AT alejandroroldanalzate enhancementofcerebrovascular4dflowmrivelocityfieldsusingmachinelearningandcomputationalfluiddynamicssimulationdata AT kevinmjohnson enhancementofcerebrovascular4dflowmrivelocityfieldsusingmachinelearningandcomputationalfluiddynamicssimulationdata
_version_	1718383067995832320

Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data

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