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...
Guardado en:
Autores principales: | David R. Rutkowski, Alejandro Roldán-Alzate, Kevin M. Johnson |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/d295f749fc4349b59b3d4283af72621a |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
COMPUTATIONAL FLUID DYNAMICS SIMULATION OF ABRASIVE FLOW MACHINING OF BIOMATERIALS
por: Sai Venkata Phanindra Chary, et al.
Publicado: (2021) -
Computational particle fluid dynamics simulation of biomass gasification in an entrained flow gasifier
por: Ramesh Timsina, et al.
Publicado: (2021) -
Simulating fluid flow in complex porous materials by integrating the governing equations with deep-layered machines
por: Serveh Kamrava, et al.
Publicado: (2021) -
Flow of magnetohydrodynamic viscous fluid by curved configuration with non-linear boundary driven velocity
por: K.M. Sanni, et al.
Publicado: (2021) -
Fluid–structure interaction simulations of a wind turbine rotor in complex flows, validated through field experiments
por: Christian Grinderslev, et al.
Publicado: (2021)