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Calibration and Analysis of an Acoustophoresis Based Cell Separator

Elmkvist, Dennis LU and Osmani, Veton LU (2017) BMEM01 20172
Department of Biomedical Engineering
Abstract
Acoustophoresis has proven to be a promising technique when it comes to separation of particles or cells in a microchip. One of the main areas of interest is the separation of so called circulating tumor cells (CTCs) from whole blood - which could serve as an indication of early stage cancer or in general be studied in order to obtain more information about these cells.

However, one issue with this technique has been the lack of easily accessible calibration. One would, for example, like to know the exact relation between the voltage transmitted to the piezo element (driving voltage) and acoustic energy that in turn can be used to obtain a desired trajectory of the particles or cells during separation - as they change a given driving... (More)
Acoustophoresis has proven to be a promising technique when it comes to separation of particles or cells in a microchip. One of the main areas of interest is the separation of so called circulating tumor cells (CTCs) from whole blood - which could serve as an indication of early stage cancer or in general be studied in order to obtain more information about these cells.

However, one issue with this technique has been the lack of easily accessible calibration. One would, for example, like to know the exact relation between the voltage transmitted to the piezo element (driving voltage) and acoustic energy that in turn can be used to obtain a desired trajectory of the particles or cells during separation - as they change a given driving voltage.

The main purpose of this thesis was to create an user-friendly software that can be used to calibrate this technique and would consist of several steps, were perhaps the main one is to find the distance from the channel wall to the closest particle trajectory when a different driving voltage is applied.

In order to obtain this, two different image-analyzing techniques were used on MATLAB: Findpeaks and Houghlines. The first one was chosen because it seemed to be the most promising method while the latter was chosen as a comparison and partly as it is the most famous image-analyzing methods known today.

Calibration is possible by obtaining the exact relation between the driving voltage and acoustic energy, which is possible by plotting a fitted curve to experimental data collected from either Findpeaks or Houghlines. With a few exceptions, the fitted curve matched (e.g. by passing through) the experimental data from Findpeaks significantly better compared to Houghlines.

Several observations were made during this study: variations in particle bead size and concentration affected the relation mentioned above, even though the latter should not, based on the equations used. In addition, the repeatability of the system was highly accurate when the same settings were used and flow rate did not have any major effect on the calibration.

Different type of microscopes, cameras and pump-systems were used to find the optimal option for calibration. Also, fluorescent particles were used for comparison with polymer beads during calibration. The outcome of this study was that no reasonable calibration could be obtained when the fluorescence particles were used and that microscope and camera were not always the main issue but rather the choice of pump-system, fluid velocity and the condition of the microchip.

In addition, a stability test was made in order to study whether the trajectories of the particles change when a constant voltage was applied during several minutes. No significant movements of the particle trajectories were observed in this study during usage of a pressure-driven flow system. (Less)
Popular Abstract (Swedish)
Kalibreringsananlys​ ​av​ ​akustofores​ ​i​ ​mikrokanal

Varje år blir miljontals människor diagnostiserade med cancer och det arbetas intensivt med att hitta behandlingar och mediciner för detta genom att undersöka hur cancer fungerar. Då är det även viktigt att man vid forskningen kan lita på sina resultat. I ett av forskningsområdena används ljud för att flytta celler i en kanal, kallat akustofores. Denna metod tillämpas bl.a. för att separera cirkulerande tumörceller från blodprov, vilket sedan kan användas för fortsatta studier av dessa celltyper eller för att följa behandlingen av cancerpatienter.
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author
Elmkvist, Dennis LU and Osmani, Veton LU
supervisor
organization
course
BMEM01 20172
year
type
H2 - Master's Degree (Two Years)
subject
keywords
acoustophoresis, microfluidics, calibration, image analysis, MATLAB, Findpeaks, Houghlines, acoustic energy, acoustic radiation force, CTC-chip, particle trajectory/band, fluorescence detection, reflections, pressure-driven flow system
language
English
additional info
2017-21
id
8927524
date added to LUP
2017-11-20 11:52:56
date last changed
2017-11-20 11:52:56
@misc{8927524,
  abstract     = {Acoustophoresis has proven to be a promising technique when it comes to separation of particles or cells in a microchip. One of the main areas of interest is the separation of so called circulating tumor cells (CTCs) from whole blood - which could serve as an indication of early stage cancer or in general be studied in order to obtain more information about these cells.

However, one issue with this technique has been the lack of easily accessible calibration. One would, for example, like to know the exact relation between the voltage transmitted to the piezo element (driving voltage) and acoustic energy that in turn can be used to obtain a desired trajectory of the particles or cells during separation - as they change a given driving voltage.

The main purpose of this thesis was to create an user-friendly software that can be used to calibrate this technique and would consist of several steps, were perhaps the main one is to find the distance from the channel wall to the closest particle trajectory when a different driving voltage is applied.

In order to obtain this, two different image-analyzing techniques were used on MATLAB: Findpeaks and Houghlines. The first one was chosen because it seemed to be the most promising method while the latter was chosen as a comparison and partly as it is the most famous image-analyzing methods known today.

Calibration is possible by obtaining the exact relation between the driving voltage and acoustic energy, which is possible by plotting a fitted curve to experimental data collected from either Findpeaks or Houghlines. With a few exceptions, the fitted curve matched (e.g. by passing through) the experimental data from Findpeaks significantly better compared to Houghlines.

Several observations were made during this study: variations in particle bead size and concentration affected the relation mentioned above, even though the latter should not, based on the equations used. In addition, the repeatability of the system was highly accurate when the same settings were used and flow rate did not have any major effect on the calibration.

Different type of microscopes, cameras and pump-systems were used to find the optimal option for calibration. Also, fluorescent particles were used for comparison with polymer beads during calibration. The outcome of this study was that no reasonable calibration could be obtained when the fluorescence particles were used and that microscope and camera were not always the main issue but rather the choice of pump-system, fluid velocity and the condition of the microchip.

In addition, a stability test was made in order to study whether the trajectories of the particles change when a constant voltage was applied during several minutes. No significant movements of the particle trajectories were observed in this study during usage of a pressure-driven flow system.},
  author       = {Elmkvist, Dennis and Osmani, Veton},
  keyword      = {acoustophoresis,microfluidics,calibration,image analysis,MATLAB,Findpeaks,Houghlines,acoustic energy,acoustic radiation force,CTC-chip,particle trajectory/band,fluorescence detection,reflections,pressure-driven flow system},
  language     = {eng},
  note         = {Student Paper},
  title        = {Calibration and Analysis of an Acoustophoresis Based Cell Separator},
  year         = {2017},
}