Matching Items (6)
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- All Subjects: Particle Image Velocimetry
- Creators: Adrian, Ronald
Description
A method of determining nanoparticle temperature through fluorescence intensity levels is described. Intracellular processes are often tracked through the use of fluorescence tagging, and ideal temperatures for many of these processes are unknown. Through the use of fluorescence-based thermometry, cellular processes such as intracellular enzyme movement can be studied and their respective temperatures established simultaneously. Polystyrene and silica nanoparticles are synthesized with a variety of temperature-sensitive dyes such as BODIPY, rose Bengal, Rhodamine dyes 6G, 700, and 800, and Nile Blue A and Nile Red. Photographs are taken with a QImaging QM1 Questar EXi Retiga camera while particles are heated from 25 to 70 C and excited at 532 nm with a Coherent DPSS-532 laser. Photographs are converted to intensity images in MATLAB and analyzed for fluorescence intensity, and plots are generated in MATLAB to describe each dye's intensity vs temperature. Regression curves are created to describe change in fluorescence intensity over temperature. Dyes are compared as nanoparticle core material is varied. Large particles are also created to match the camera's optical resolution capabilities, and it is established that intensity values increase proportionally with nanoparticle size. Nile Red yielded the closest-fit model, with R2 values greater than 0.99 for a second-order polynomial fit. By contrast, Rhodamine 6G only yielded an R2 value of 0.88 for a third-order polynomial fit, making it the least reliable dye for temperature measurements using the polynomial model. Of particular interest in this work is Nile Blue A, whose fluorescence-temperature curve yielded a much different shape from the other dyes. It is recommended that future work describe a broader range of dyes and nanoparticle sizes, and use multiple excitation wavelengths to better quantify each dye's quantum efficiency. Further research into the effects of nanoparticle size on fluorescence intensity levels should be considered as the particles used here greatly exceed 2 ìm. In addition, Nile Blue A should be further investigated as to why its fluorescence-temperature curve did not take on a characteristic shape for a temperature-sensitive dye in these experiments.
ContributorsTomforde, Christine (Author) / Phelan, Patrick (Thesis advisor) / Dai, Lenore (Committee member) / Adrian, Ronald (Committee member) / Arizona State University (Publisher)
Created2011
Description
Microfluidics is the study of fluid flow at very small scales (micro -- one millionth of a meter) and is prevalent in many areas of science and engineering. Typical applications include lab-on-a-chip devices, microfluidic fuel cells, and DNA separation technologies. Many of these microfluidic devices rely on micron-resolution velocimetry measurements to improve microchannel design and characterize existing devices. Methods such as micro particle imaging velocimetry (microPIV) and micro particle tracking velocimetry (microPTV) are mature and established methods for characterization of steady 2D flow fields. Increasingly complex microdevices require techniques that measure unsteady and/or three dimensional velocity fields. This dissertation presents a method for three-dimensional velocimetry of unsteady microflows based on spinning disk confocal microscopy and depth scanning of a microvolume. High-speed 2D unsteady velocity fields are resolved by acquiring images of particle motion using a high-speed CMOS camera and confocal microscope. The confocal microscope spatially filters out of focus light using a rotating disk of pinholes placed in the imaging path, improving the ability of the system to resolve unsteady microPIV measurements by improving the image and correlation signal to noise ratio. For 3D3C measurements, a piezo-actuated objective positioner quickly scans the depth of the microvolume and collects 2D image slices, which are stacked into 3D images. Super resolution microPIV interrogates these 3D images using microPIV as a predictor field for tracking individual particles with microPTV. The 3D3C diagnostic is demonstrated by measuring a pressure driven flow in a three-dimensional expanding microchannel. The experimental velocimetry data acquired at 30 Hz with instantaneous spatial resolution of 4.5 by 4.5 by 4.5 microns agrees well with a computational model of the flow field. The technique allows for isosurface visualization of time resolved 3D3C particle motion and high spatial resolution velocity measurements without requiring a calibration step or reconstruction algorithms. Several applications are investigated, including 3D quantitative fluorescence imaging of isotachophoresis plugs advecting through a microchannel and the dynamics of reaction induced colloidal crystal deposition.
ContributorsKlein, Steven Adam (Author) / Posner, Jonathan D (Thesis advisor) / Adrian, Ronald (Committee member) / Chen, Kangping (Committee member) / Devasenathipathy, Shankar (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2011
Description
Image segmentation is of great importance and value in many applications. In computer vision, image segmentation is the tool and process of locating objects and boundaries within images. The segmentation result may provide more meaningful image data. Generally, there are two fundamental image segmentation algorithms: discontinuity and similarity. The idea behind discontinuity is locating the abrupt changes in intensity of images, as are often seen in edges or boundaries. Similarity subdivides an image into regions that fit the pre-defined criteria. The algorithm utilized in this thesis is the second category.
This study addresses the problem of particle image segmentation by measuring the similarity between a sampled region and an adjacent region, based on Bhattacharyya distance and an image feature extraction technique that uses distribution of local binary patterns and pattern contrasts. A boundary smoothing process is developed to improve the accuracy of the segmentation. The novel particle image segmentation algorithm is tested using four different cases of particle image velocimetry (PIV) images. The obtained experimental results of segmentations provide partitioning of the objects within 10 percent error rate. Ground-truth segmentation data, which are manually segmented image from each case, are used to calculate the error rate of the segmentations.
This study addresses the problem of particle image segmentation by measuring the similarity between a sampled region and an adjacent region, based on Bhattacharyya distance and an image feature extraction technique that uses distribution of local binary patterns and pattern contrasts. A boundary smoothing process is developed to improve the accuracy of the segmentation. The novel particle image segmentation algorithm is tested using four different cases of particle image velocimetry (PIV) images. The obtained experimental results of segmentations provide partitioning of the objects within 10 percent error rate. Ground-truth segmentation data, which are manually segmented image from each case, are used to calculate the error rate of the segmentations.
ContributorsHan, Dongmin (Author) / Frakes, David (Thesis advisor) / Adrian, Ronald (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2015
Description
Cerebral aneurysms are pathological balloonings of blood vessels in the brain, commonly found in the arterial network at the base of the brain. Cerebral aneurysm rupture can lead to a dangerous medical condition, subarachnoid hemorrhage, that is associated with high rates of morbidity and mortality. Effective evaluation and management of cerebral aneurysms is therefore essential to public health. The goal of treating an aneurysm is to isolate the aneurysm from its surrounding circulation, thereby preventing further growth and rupture. Endovascular treatment for cerebral aneurysms has gained popularity over traditional surgical techniques due to its minimally invasive nature and shorter associated recovery time. The hemodynamic modifications that the treatment effects can promote thrombus formation within the aneurysm leading to eventual isolation. However, different treatment devices can effect very different hemodynamic outcomes in aneurysms with different geometries.
Currently, cerebral aneurysm risk evaluation and treatment planning in clinical practice is largely based on geometric features of the aneurysm including the dome size, dome-to-neck ratio, and parent vessel geometry. Hemodynamics, on the other hand, although known to be deeply involved in cerebral aneurysm initiation and progression, are considered to a lesser degree. Previous work in the field of biofluid mechanics has demonstrated that geometry is a driving factor behind aneurysmal hemodynamics.
The goal of this research is to develop a more combined geometric/hemodynamic basis for informing clinical decisions. Geometric main effects were analyzed to quantify contributions made by geometric factors that describe cerebral aneurysms (i.e., dome size, dome-to-neck ratio, and inflow angle) to clinically relevant hemodynamic responses (i.e., wall shear stress, root mean square velocity magnitude and cross-neck flow). Computational templates of idealized bifurcation and sidewall aneurysms were created to satisfy a two-level full factorial design, and examined using computational fluid dynamics. A subset of the computational bifurcation templates was also translated into physical models for experimental validation using particle image velocimetry. The effects of geometry on treatment were analyzed by virtually treating the aneurysm templates with endovascular devices. The statistical relationships between geometry, treatment, and flow that emerged have the potential to play a valuable role in clinical practice.
Currently, cerebral aneurysm risk evaluation and treatment planning in clinical practice is largely based on geometric features of the aneurysm including the dome size, dome-to-neck ratio, and parent vessel geometry. Hemodynamics, on the other hand, although known to be deeply involved in cerebral aneurysm initiation and progression, are considered to a lesser degree. Previous work in the field of biofluid mechanics has demonstrated that geometry is a driving factor behind aneurysmal hemodynamics.
The goal of this research is to develop a more combined geometric/hemodynamic basis for informing clinical decisions. Geometric main effects were analyzed to quantify contributions made by geometric factors that describe cerebral aneurysms (i.e., dome size, dome-to-neck ratio, and inflow angle) to clinically relevant hemodynamic responses (i.e., wall shear stress, root mean square velocity magnitude and cross-neck flow). Computational templates of idealized bifurcation and sidewall aneurysms were created to satisfy a two-level full factorial design, and examined using computational fluid dynamics. A subset of the computational bifurcation templates was also translated into physical models for experimental validation using particle image velocimetry. The effects of geometry on treatment were analyzed by virtually treating the aneurysm templates with endovascular devices. The statistical relationships between geometry, treatment, and flow that emerged have the potential to play a valuable role in clinical practice.
ContributorsNair, Priya (Author) / Frakes, David (Thesis advisor) / Vernon, Brent (Committee member) / Chong, Brian (Committee member) / Pizziconi, Vincent (Committee member) / Adrian, Ronald (Committee member) / Arizona State University (Publisher)
Created2016
Description
Particle Image Velocimetry (PIV) has become a cornerstone of modern experimental fluid mechanics due to its unique ability to resolve the entire instantaneous two-dimensional velocity field of an experimental flow. However, this methodology has historically been omitted from undergraduate curricula due to the significant cost of research-grade PIV systems and safety considerations stemming from the high-power Nd-YAG lasers typically implemented by PIV systems. In the following undergraduate thesis, a low-cost model of a PIV system is designed to be used within the context of an undergraduate fluid mechanics lab. The proposed system consists of a Hele-Shaw water tunnel, a high-power LED lighting source, and a modern smartphone camera. Additionally, a standalone application was developed to perform the necessary image processing as well as to perform Particle Streak Velocimetry (PSV) and PIV image analysis. Ultimately, the proposed system costs $229.33 and can replicate modern PIV techniques albeit for simple flow scenarios.
ContributorsZamora, Matthew Alan (Author) / Adrian, Ronald (Thesis director) / Kim, Jeonglae (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The application of novel visualization and modeling methods to the study of cardiovascular disease is vital to the development of innovative diagnostic techniques, including those that may aid in the early detection and prevention of cardiovascular disorders. This dissertation focuses on the application of particle image velocimetry (PIV) to the study of intracardiac hemodynamics. This is accomplished primarily though the use of ultrasound based PIV, which allows for in vivo visualization of intracardiac flow without the requirement for optical access, as is required with traditional camera-based PIV methods.
The fundamentals of ultrasound PIV are introduced, including experimental methods for its implementation as well as a discussion on estimating and mitigating measurement error. Ultrasound PIV is then compared to optical PIV; this is a highly developed technique with proven accuracy; through rigorous examination it has become the “gold standard” of two-dimensional flow visualization. Results show good agreement between the two methods.
Using a mechanical left heart model, a multi-plane ultrasound PIV technique is introduced and applied to quantify a complex, three-dimensional flow that is analogous to the left intraventricular flow. Changes in ventricular flow dynamics due to the rotational orientation of mechanical heart valves are studied; the results demonstrate the importance of multi-plane imaging techniques when trying to assess the strongly three-dimensional intraventricular flow.
The potential use of ultrasound PIV as an early diagnosis technique is demonstrated through the development of a novel elasticity estimation technique. A finite element analysis routine is couple with an ensemble Kalman filter to allow for the estimation of material elasticity using forcing and displacement data derived from PIV. Results demonstrate that it is possible to estimate elasticity using forcing data derived from a PIV vector field, provided vector density is sufficient.
The fundamentals of ultrasound PIV are introduced, including experimental methods for its implementation as well as a discussion on estimating and mitigating measurement error. Ultrasound PIV is then compared to optical PIV; this is a highly developed technique with proven accuracy; through rigorous examination it has become the “gold standard” of two-dimensional flow visualization. Results show good agreement between the two methods.
Using a mechanical left heart model, a multi-plane ultrasound PIV technique is introduced and applied to quantify a complex, three-dimensional flow that is analogous to the left intraventricular flow. Changes in ventricular flow dynamics due to the rotational orientation of mechanical heart valves are studied; the results demonstrate the importance of multi-plane imaging techniques when trying to assess the strongly three-dimensional intraventricular flow.
The potential use of ultrasound PIV as an early diagnosis technique is demonstrated through the development of a novel elasticity estimation technique. A finite element analysis routine is couple with an ensemble Kalman filter to allow for the estimation of material elasticity using forcing and displacement data derived from PIV. Results demonstrate that it is possible to estimate elasticity using forcing data derived from a PIV vector field, provided vector density is sufficient.
ContributorsWesterdale, John Curtis (Author) / Adrian, Ronald (Thesis advisor) / Belohlavek, Marek (Committee member) / Squires, Kyle (Committee member) / Trimble, Steve (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2015