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- All Subjects: engineering
- All Subjects: Cerebral Aneurysms
Description
Single cell phenotypic heterogeneity studies reveal more information about the pathogenesis process than conventional bulk methods. Furthermore, investigation of the individual cellular response mechanism during rapid environmental changes can only be achieved at single cell level. By enabling the study of cellular morphology, a single cell three-dimensional (3D) imaging system can be used to diagnose fatal diseases, such as cancer, at an early stage. One proven method, CellCT, accomplishes 3D imaging by rotating a single cell around a fixed axis. However, some existing cell rotating mechanisms require either intricate microfabrication, and some fail to provide a suitable environment for living cells. This thesis develops a microvorterx chamber that allows living cells to be rotated by hydrodynamic alone while facilitating imaging access. In this thesis work, 1) the new chamber design was developed through numerical simulation. Simulations revealed that in order to form a microvortex in the side chamber, the ratio of the chamber opening to the channel width must be smaller than one. After comparing different chamber designs, the trapezoidal side chamber was selected because it demonstrated controllable circulation and met the imaging requirements. Microvortex properties were not sensitive to the chambers with interface angles ranging from 0.32 to 0.64. A similar trend was observed when chamber heights were larger than chamber opening. 2) Micro-particle image velocimetry was used to characterize microvortices and validate simulation results. Agreement between experimentation and simulation confirmed that numerical simulation was an effective method for chamber design. 3) Finally, cell rotation experiments were performed in the trapezoidal side chamber. The experimental results demonstrated cell rotational rates ranging from 12 to 29 rpm for regular cells. With a volumetric flow rate of 0.5 µL/s, an irregular cell rotated at a mean rate of 97 ± 3 rpm. Rotational rates can be changed by altering inlet flow rates.
ContributorsZhang, Wenjie (Author) / Frakes, David (Thesis advisor) / Meldrum, Deirdre (Thesis advisor) / Chao, Shih-hui (Committee member) / Wang, Xiao (Committee member) / Arizona State University (Publisher)
Created2011
Description
The purpose of this thesis was to explore how changes in the geometry of a bifurcating cerebral aneurysm will affect the hemodynamics in idealized models after stent treatment. This thesis explores the use of a computationally modeled Enterprise Vascular Reconstruction Device (Cordis, East Bridgewater, NJ), a high porosity and closed cell design. The models represent idealized cases of saccular aneurysms with dome sizes of either 4mm or 6mm and a dome to neck ratio of either 3:2 or 2:1. Two aneurysm contact angles are studied, one at 45 degrees and the other at 90 degrees. The stent was characterized and deployed with the use of Finite Element Analysis into each model. Computational Fluid Dynamic principles were applied in series of simulations on treated and untreated models. Data was gathered in the neck plane for the average velocity magnitude, root mean squared velocity, average flow vector angle of deflection, and the cross neck flow rate. Within the aneurysm, the average velocity magnitude, root mean squared velocity, and average pressure were calculated. Additionally, the mass flow rate at each outlet was recorded. The results of this study indicate that the Enterprise Stent was most effective in the sharper, 90 degree geometry of Model 3. Additionally, the stent had an adverse effect on the Models 1 and 4, which had the smallest neck sizes. Conclusions are that the Enterprise Stent, as a stand-alone treatment method is only reliable in situations that take advantage of its design.
ContributorsThomas, Kyle Andrew (Author) / Frakes, David (Thesis director) / LaBelle, Jeffrey (Committee member) / Babiker, Haithem (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2013-05
Description
Magnetic Resonance Imaging using spiral trajectories has many advantages in speed, efficiency in data-acquistion and robustness to motion and flow related artifacts. The increase in sampling speed, however, requires high performance of the gradient system. Hardware inaccuracies from system delays and eddy currents can cause spatial and temporal distortions in the encoding gradient waveforms. This causes sampling discrepancies between the actual and the ideal k-space trajectory. Reconstruction assuming an ideal trajectory can result in shading and blurring artifacts in spiral images. Current methods to estimate such hardware errors require many modifications to the pulse sequence, phantom measurements or specialized hardware. This work presents a new method to estimate time-varying system delays for spiral-based trajectories. It requires a minor modification of a conventional stack-of-spirals sequence and analyzes data collected on three orthogonal cylinders. The method is fast, robust to off-resonance effects, requires no phantom measurements or specialized hardware and estimate variable system delays for the three gradient channels over the data-sampling period. The initial results are presented for acquired phantom and in-vivo data, which show a substantial reduction in the artifacts and improvement in the image quality.
ContributorsBhavsar, Payal (Author) / Pipe, James G (Thesis advisor) / Frakes, David (Committee member) / Kodibagkar, Vikram (Committee member) / Arizona State University (Publisher)
Created2013
Description
Cerebral aneurysms, also known as intracranial aneurysms, are sac-like lesions in the arteries of the brain that can rupture to cause subarachnoid hemorrhaging, damaging and killing brain cells. Metal coil embolization has been traditionally used to occlude and treat cerebral aneurysms to limited success, but polymer embolization has been suggested, because it can provide a greater fraction of occlusion. One such polymer with low cytotoxicity is poly(propylene glycol)diacrylate (PPODA) crosslinked via Michael-type addition with pentaerythritol tetrakis(3-mercaptopropionate) (QT). This study was performed to examine the behavior of PPODA-QT gel in vitro under pulsatile flow emulating physiological conditions. An idealized cerebral aneurysm flow model was designed based on geometries associated with an increase in rupture risk. Pressure was monitored at the apex of the aneurysm dome for varied flow rates and polymer filling fractions of 32.4, 78.2, and 100%. The results indicate that the amount of PPODA-QT deployed into the aneurysm decreases the peak-to-peak oscillation in pressure at the aneurysm wall by an inverse proportion. The 32.4 and 78.2% treatments did not significantly decrease the mean pressure applied to the aneurysm dome, but the 100% treatment greatly reduced it by diverting flow. This study indicates that the maximum filling fraction after swelling of PPODA-QT polymer should be deployed into the aneurysmal sac for treatment.
ContributorsWorkman, Christopher David (Author) / Vernon, Brent (Thesis director) / Frakes, David (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05
Customized Endovascular Treatment Fixture Construction for Patient-Specific Cerebral Aneurysm Repair
Description
Cerebral aneurysms are pathological bulges in blood vessels of the brain that can rupture and cause brain damage or death. Treating aneurysms by isolating them from circulation can prevent aneurysm rupture. Endovascular techniques for cerebral aneurysm treatment are preferred because they are minimally invasive and have a shorter recovery time, and endovascular coiling is considered the gold standard as a result. The coils used in endovascular treatment come in standard shapes and sizes, mass-manufactured by medical device companies. Clinicians select the coils for treatment based on the aneurysm volume. However, cerebral aneurysms have unique shapes and dimensions, and vary on a patient-specific basis. Therefore, customizing the coils to fit a unique aneurysm morphology by using shape memory alloys could potentially improve endovascular treatment outcomes. In order to shape set a shape memory alloy into a customized coil configuration a fixture based on the aneurysm morphology must first be developed. Digital surface models of aneurysm patient cases were collected from an online repository and isolated from surrounding vasculature. Anchors used to assist in winding coils around these models were then added to create a computational fixture model. These fixtures were 3D printed in stainless steel, and tested on their ability to maintain their shape after being exposed to high temperatures needed in shape setting processes. The study demonstrated that customized fixtures can be created from patient-specific images or models, and manufactured with high levels of accuracy without deformation at high temperatures. The results suggest that 3D printed stainless steel fixtures could be used to develop customized endovascular coils for cerebral aneurysm treatment.
ContributorsHess, Ryan Ambrose (Author) / Kleim, Jeff (Thesis director) / Nair, Priya (Committee member) / Frakes, David (Committee member) / Barrett, The Honors College (Contributor)
Created2017-05
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
Cigarette smoking remains a major global public health issue. This is partially due to the chronic and relapsing nature of tobacco use, which contributes to the approximately 90% quit attempt failure rate. The recent rise in mobile technologies has led to an increased ability to frequently measure smoking behaviors and related constructs over time, i.e., obtain intensive longitudinal data (ILD). Dynamical systems modeling and system identification methods from engineering offer a means to leverage ILD in order to better model dynamic smoking behaviors. In this dissertation, two sets of dynamical systems models are estimated using ILD from a smoking cessation clinical trial: one set describes cessation as a craving-mediated process; a second set was reverse-engineered and describes a psychological self-regulation process in which smoking activity regulates craving levels. The estimated expressions suggest that self-regulation more accurately describes cessation behavior change, and that the psychological self-regulator resembles a proportional-with-filter controller. In contrast to current clinical practice, adaptive smoking cessation interventions seek to personalize cessation treatment over time. An intervention of this nature generally reflects a control system with feedback and feedforward components, suggesting its design could benefit from a control systems engineering perspective. An adaptive intervention is designed in this dissertation in the form of a Hybrid Model Predictive Control (HMPC) decision algorithm. This algorithm assigns counseling, bupropion, and nicotine lozenges each day to promote tracking of target smoking and craving levels. Demonstrated through a diverse series of simulations, this HMPC-based intervention can aid a successful cessation attempt. Objective function weights and three-degree-of-freedom tuning parameters can be sensibly selected to achieve intervention performance goals despite strict clinical and operational constraints. Such tuning largely affects the rate at which peak bupropion and lozenge dosages are assigned; total post-quit smoking levels, craving offset, and other performance metrics are consequently affected. Overall, the interconnected nature of the smoking and craving controlled variables facilitate the controller's robust decision-making capabilities, even despite the presence of noise or plant-model mismatch. Altogether, this dissertation lays the conceptual and computational groundwork for future efforts to utilize engineering concepts to further study smoking behaviors and to optimize smoking cessation interventions.
ContributorsTimms, Kevin Patrick (Author) / Rivera, Daniel E (Thesis advisor) / Frakes, David (Committee member) / Nielsen, David R (Committee member) / Arizona State University (Publisher)
Created2014