The importance of blood rheology in patient-specific computational fluid dynamics simulation of stenotic carotid arteries

2020 ◽  
Vol 19 (5) ◽  
pp. 1477-1490 ◽  
Author(s):  
Jessica Benitez Mendieta ◽  
Davide Fontanarosa ◽  
Jiaqiu Wang ◽  
Phani Kumari Paritala ◽  
Tim McGahan ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Carotid Arteries ◽  
Blood Rheology ◽  
Dynamics Simulation ◽  
Patient Specific ◽  
Computational Fluid Dynamics Simulation

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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
David R. Rutkowski ◽  
Alejandro Roldán-Alzate ◽  
Kevin M. Johnson
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Velocity Fields ◽  
Dynamics Simulation ◽  
Patient Specific ◽  
4D Flow Mri ◽  
4D Flow ◽  
Flow Phase ◽  
Flow Mri ◽  
Trained Network

AbstractBlood 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.

scholarly journals MP05-16 COMPUTATIONAL FLUID DYNAMICS SIMULATION OF MALE VOIDING: A NOVEL METHOD USING REAL-TIME MAGNETIC RESONANCE IMAGE

2016 ◽  
Vol 195 (4S) ◽  
Author(s):  
Shigehiro Soh ◽  
Toshiyuki Iwahata ◽  
Keisuke Suzuki ◽  
Tomohiro Kobayashi ◽  
Shin Takeshi ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Magnetic Resonance ◽  
Real Time ◽  
Magnetic Resonance Image ◽  
Dynamics Simulation ◽  
Computational Fluid Dynamics Simulation ◽  
Novel Method
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