Some problems on the flow direction of orbital ophthalmic artery in measuring flow velocities using ultrasonic pulsed Doppler methods

We introduce a Python based software tool to measure surface flow velocities and to estimate discharge eventually. Minimum needed input are image sequences, some camera parameters and object space information to scale the image measurements. Reference information can be provided either indirectly via ground control point measurements or directly providing camera pose parameters. To improve the reliability and density of velocity measurements the area of interest has to be masked for image velocimetry. This can either be performed with a binary mask file or considering a 3D point cloud, for instance retrieved with Structure from Motion (SfM) photogrammetry, describing the region of interest. The tracking task can be done with particle image velocimetry (PIV) considering small interrogation regions or using particle tracking velocimetry (PTV) and thus detecting and tracking features at the water surface. To improve the robustness of the tracking results, filtering can be applied that implements statistical information about the flow direction, flow steadiness and average velocities.The FlowVeloTool has been tested with two different datasets; one at a gauging station and one at a natural river reach. Thereby, UAV and terrestrial data were acquired and processed. Velocities can be estimated with an accuracy of 0.01&#160;m/s. If information about the river topography and bathymetry are available, as in our demonstration, discharge can be estimated with an error ranging from 5 to 31&#160;% (Eltner et al. 2019). Besides these results we demonstrate further developments of the FlowVeloTool regarding filtering of tracking results, discharge estimation, and processing of time series. Furthermore, we illustrate that thermal data can be used, as well, with our tool to retrieve river surface velocities.&#160;Eltner, A., Sardemann, H., and Grundmann, J.: Flow velocity and discharge measurement in rivers using terrestrial and UAV imagery, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-289, 2019.

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Ophthalmic Artery Flow Direction on Color Flow Duplex Imaging Is Highly Specific for Severe Carotid Stenosis

Journal of Neuroimaging ◽

10.1111/j.1552-6569.2002.tb00082.x ◽

2002 ◽

Vol 12 (1) ◽

pp. 5-8 ◽

Cited By ~ 15

Author(s):

Patrick S. Reynolds ◽

Jason P. Greenberg ◽

Li-Ming Lien ◽

Dana C. Meads ◽

Lawrence G. Myers ◽

...

Keyword(s):

Carotid Stenosis ◽

Ophthalmic Artery ◽

Flow Direction ◽

Color Flow ◽

Duplex Imaging ◽

Artery Flow

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Reduction of the Clutter Component in Doppler Ultrasound Signals Based on Singular Value Decomposition: A Simulation Study

Ultrasonic Imaging ◽

10.1177/016173469701900101 ◽

1997 ◽

Vol 19 (1) ◽

pp. 1-18 ◽

Cited By ~ 61

Author(s):

Léon A.F. Ledoux ◽

Peter J. Brands ◽

Arnold P.G. Hoeks

Keyword(s):

Blood Flow ◽

Singular Value Decomposition ◽

Doppler Ultrasound ◽

Singular Value ◽

Pulsed Doppler ◽

Rf Signals ◽

Blood Flow Velocities ◽

Value Decomposition ◽

High Pass ◽

Flow Velocities

In pulsed Doppler ultrasound systems, the ultrasound radiofrequency (RF) signals received can be employed to estimate noninvasively the time-dependent blood flow velocity distribution within an artery. The RF signals are composed of signals originating from clutter (e.g., vessel walls) and scatterers (e.g., red blood cells). The clutter, which is induced by stationary or slowly-moving structure interfaces, must be suppressed to get reliable estimates of the mean blood flow velocities. In conventional pulsed Doppler systems, this is achieved with a static temporal high-pass filter. The static cut-off frequency and the roll-off of these filters cause the clutter not always to be optimally suppressed. This paper introduces a clutter removal filter that is based on Singular Value Decomposition (SVD). Unlike conventional high-pass filters, which take into account only the information of the temporal direction, the SVD filter makes use of the information of the temporal and spatial directions. The advantage of this approach is that it does not matter where the clutter is located in the RF signal. The performance of the SVD filter is examined with computer-generated Doppler RF signals. The results are compared with those of a standard linear regression (SLR) filter. The performance of the SVD filter is good, especially if a large temporal window (i.e., approximately 100 RF signals) is applied, which improves the performance for low blood flow velocities. A major disadvantage of the SVD filter is its computational complexity, which increases considerably for larger temporal windows.

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