Doppler angler and flow velocity estimation using conventional single-probing-beam optical Doppler tomography

2003 ◽  
Author(s):  
Daqing Piao ◽  
Quing Zhu
Keyword(s):  
Flow Velocity ◽  
Velocity Estimation ◽  
Doppler Tomography ◽  
Optical Doppler Tomography

scholarly journals Noninvasive imaging of in vivo blood flow velocity using optical Doppler tomography

1997 ◽  
Vol 22 (14) ◽  
pp. 1119 ◽  
Author(s):  
Zhongping Chen ◽  
Thomas E. Milner ◽  
Shyam Srinivas ◽  
Xiaojun Wang ◽  
Arash Malekafzali ◽  
...  
Keyword(s):  
Blood Flow ◽  
Flow Velocity ◽  
Blood Flow Velocity ◽  
Noninvasive Imaging ◽  
Doppler Tomography ◽  
Optical Doppler Tomography

scholarly journals Characterization of fluid flow velocity by optical Doppler tomography

1995 ◽  
Vol 20 (11) ◽  
pp. 1337 ◽  
Author(s):  
X. J. Wang ◽  
T. E. Milner ◽  
J. S. Nelson
Keyword(s):  
Fluid Flow ◽  
Flow Velocity ◽  
Doppler Tomography ◽  
Fluid Flow Velocity ◽  
Optical Doppler Tomography

scholarly journals Flow velocity quantification by exploiting the principles of the Doppler effect and magnetic particle imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dennis Pantke ◽  
Florian Mueller ◽  
Sebastian Reinartz ◽  
Fabian Kiessling ◽  
Volkmar Schulz
Keyword(s):  
Flow Velocity ◽  
Magnetic Particle ◽  
Imaging Techniques ◽  
Radiation Risk ◽  
Medical Application ◽  
Velocity Estimation ◽  
Magnetic Particle Imaging ◽  
Measurable Velocity ◽  
Depth Dependency ◽  
Particle Imaging

AbstractChanges in blood flow velocity play a crucial role during pathogenesis and progression of cardiovascular diseases. Imaging techniques capable of assessing flow velocities are clinically applied but are often not accurate, quantitative, and reliable enough to assess fine changes indicating the early onset of diseases and their conversion into a symptomatic stage. Magnetic particle imaging (MPI) promises to overcome these limitations. Existing MPI-based techniques perform velocity estimation on the reconstructed images, which restricts the measurable velocity range. Therefore, we developed a novel velocity quantification method by adapting the Doppler principle to MPI. Our method exploits the velocity-dependent frequency shift caused by a tracer motion-induced modulation of the emitted signal. The fundamental theory of our method is deduced and validated by simulations and measurements of moving phantoms. Overall, our method enables robust velocity quantification within milliseconds, with high accuracy, no radiation risk, no depth-dependency, and extended range compared to existing MPI-based velocity quantification techniques, highlighting the potential of our method as future medical application.

scholarly journals Hybrid Scour Depth Prediction Equations for Reliable Design of Bridge Piers

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2019
Author(s):  
Hossein Hamidifar ◽  
Faezeh Zanganeh-Inaloo ◽  
Iacopo Carnacina
Keyword(s):  
Flow Velocity ◽  
Critical Velocity ◽  
Depth Estimation ◽  
Hybrid Models ◽  
Velocity Estimation ◽  
Bridge Piers ◽  
Critical Flow ◽  
Scour Depth ◽  
Critical Flow Velocity ◽  
Estimation Equations

Numerous models have been proposed in the past to predict the maximum scour depth around bridge piers. These studies have all focused on the different parameters that could affect the maximum scour depth and the model accuracy. One of the main parameters individuated is the critical velocity of the approaching flow. The present study aimed at investigating the effect of different equations to determine the critical flow velocity on the accuracy of models for estimating the maximum scour depth around bridge piers. Here, 10 scour depth estimation equations, which include the critical flow velocity as one of the influencing parameters, and 8 critical velocity estimation equations were examined, for a total combination of 80 hybrid models. In addition, a sensitivity analysis of the selected scour depth equations to the critical velocity was investigated. The results of the selected models were compared with experimental data, and the best hybrid models were identified using statistical indicators. The accuracy of the best models, including YJAF-VRAD, YJAF-VARN, and YJAI-VRAD models, was also evaluated using field data available in the literature. Finally, correction factors were implied to the selected models to increase their accuracy in predicting the maximum scour depth.

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