Micro-particle Image Velocimetry for Velocity Profile Measurements of Micro Blood Flows

Pitts, Katie L.; Fenech, Marianne

doi:10.3791/50314

Cited by 12 publications

(9 citation statements)

References 19 publications

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“…Rectangular channels were chosen for the initial network instead of round ones to facilitate imaging and fabrication. When using imaging techniques with round channels, extra care is necessary to consider the shadowing effect of cells on top of each other [55], [56]. Techniques are available to create round channels, such as using metal wires or pressurized air, however these are more complicated to execute for the complex geometries that may be found in a modular network [57].…”

Section: Design Principlementioning

confidence: 99%

Optically clear biomicroviscometer with modular geometry using disposable PDMS chips

Lee-Yow

Pitts

Fenech

2017

2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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ii AbstractWe have designed and fabricated a biomicroviscometer platform for measurement of microflows of biological fluids. The biomicroviscometer combines an optically clear biocompatible polydimethylsiloxane (PDMS) channel with on-chip integrated microfluidic differential pressure sensors and capabilities of modular channel geometries. This setup allows for a direct measurement of the change in pressure and flow rate, increasing the overall accuracy of the measurement of viscosity and optical observation. We present an introduction of this combined method of measurement with different channel dimensions, using Newtonian and non-Newtonian fluids, and the corresponding calculations. This measurement technique has potential applications in measuring rheological properties at the micro level to further blood disease analysis, and lab-on-a-chip fabrication and analysis.Acknowledgements iii

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Section: Design Principlementioning

confidence: 99%

Optically clear biomicroviscometer with modular geometry using disposable PDMS chips

Lee-Yow

Pitts

Fenech

2017

2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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“…The methodology used to determine the velocity is detailed elsewhere [12]. The root mean square (RMS) error is calculated by averaging instantaneous velocity measurements for image n as follows:…”

Section: Flow Visualization and Data Analysismentioning

confidence: 99%

Untitled

Mehri¹,

Laplante²,

Mavriplis³

et al. 2014

J. Med. Biol. Eng.

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This work aims to develop a method for quantitatively analyzing red blood cell (RBC) aggregates under controlled flow conditions. Images of experiments are captured and processed in order to quantify aggregation. The experimental setup consists of RBC suspensions in blood plasma entrained by a phosphate-buffered saline solution in a 110 × 60 µm polydimethylsiloxane microchannel. The experiments are performed by varying the hematocrit (5, 10, and 15%) and the flow rate (Q = 5 and 10 µl/hr) in order to observe the effect of shear rate on RBC aggregation. Microchannel dimensions as well as fluid flow rates are determined using numerical simulations. The flow is visualized using a highspeed camera coupled to a micro particle image velocimetry system. Videos obtained with the high-speed camera are processed using a MATLAB program, with each frame analyzed separately. RBC aggregates are detected based on the image intensities and the connectivity between RBCs, using image processing techniques. The average aggregate size and distribution of RBCs for various aggregate sizes are determined for each of the shear rates and hematocrits. These aggregates are shown to be larger at low flow rates where the shear rate is small. Results from tests performed at high hematocrits also show larger RBC aggregates.

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“…A common pressure‐driven measurement in microfluidic channels is the modeling of blood flow, where some important factors are the flow rate of blood, shape of the velocity profile, as well as the shear stress on the wall of the microfluidic channel [1]. In electroosmotic‐driven applications, some important characteristics to determine are the electric potentials within the electric double layer (EDL), which is a crucial property in electrokinetic flow [10].…”

Section: Introductionmentioning

confidence: 99%

“…The need to investigate various flow characteristics in many fields of engineering is apparent [1][2][3], as characterization of microfluidic sensors is crucial to demonstrate accuracy and reliability [4]. The fluid dynamics through a microfluidic channel can be studied to quantify and visualize the developed velocity profiles [5].…”

Section: Introductionmentioning

confidence: 99%

A velocity program using the Kanade–Lucas–Tomasi feature‐tracking algorithm with demonstration for pressure and electroosmosis conditions

2022

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Investigating microfluidic flow profiles is of interest in the microfluidics field for the determination of various characteristics of a lab-on-a-chip system. Microparticle tracking velocimetry uses computational methods upon recording video footage of microfluidic flow to ultimately visualize motion within a microfluidic system across all frames of a video. Current methods are computationally expensive or require extensive instrumentation. A computational method suited to microparticle tracking applications is the robust Kanade-Lucas-Tomasi (KLT) feature-tracking algorithm. This work explores a microparticle tracking velocimetry program using the KLT feature-tracking algorithm. The developed program is demonstrated using pressure-driven and EOF and compared with the respective mathematical fluid flow models. An electrostatics analysis of EOF conditions is performed in the development of the mathematical using a Poisson's Equation solver. This analysis is used to quantify the zeta potential of the electroosmotic system. Overall, the KLT featuretracking algorithm presented in this work proved to be highly reliable and computationally efficient for investigations of pressure-driven and EOF in a microfluidic system.

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Micro-particle Image Velocimetry for Velocity Profile Measurements of Micro Blood Flows

Cited by 12 publications

References 19 publications

Optically clear biomicroviscometer with modular geometry using disposable PDMS chips

Optically clear biomicroviscometer with modular geometry using disposable PDMS chips

Untitled

A velocity program using the Kanade–Lucas–Tomasi feature‐tracking algorithm with demonstration for pressure and electroosmosis conditions

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