Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Frakes, David
Description
Owing to the surge in development of endovascular devices such as coils and flow diverter stents, doctors are inclined to approach surgical cases non-invasively more often than before. Treating brain aneurysms as a bulging of a weakened area of a blood vessel is no exception. Therefore, promoting techniques that can help surgeons have a better idea of treatment outcomes are of invaluable importance.
In order to investigate the effects of these devices on intra-aneurysmal hemodynamics, the conventional computational fluid dynamics (CFD) approach uses the explicit geometry of the device within an aneurysm and discretizes the fluid domain to solve the Navier-Stokes equations. However, since the devices are made of small struts, the number of mesh elements in the boundary layer region would be considerable. This cumbersome task led to the implementation of the porous medium assumption. In this approach, the explicit geometry of the device is eliminated, and relevant porous medium assumptions are applied. Unfortunately, as it will be shown in this research, some of the porous medium approaches used in the literature are over-simplified. For example, considering the porous domain to be homogeneous is one major drawback which leads to significant errors in capturing the intra-aneurysmal flow features. Specifically, since the devices must comply with the complex geometry of an aneurysm, the homogeneity assumption is not valid.
In this research, a novel heterogeneous porous medium approach is introduced. This results in a substantial reduction in the total number of mesh elements required to discretize the flow domain while not sacrificing the accuracy of the method by over-simplifying the utilized assumptions.
In order to investigate the effects of these devices on intra-aneurysmal hemodynamics, the conventional computational fluid dynamics (CFD) approach uses the explicit geometry of the device within an aneurysm and discretizes the fluid domain to solve the Navier-Stokes equations. However, since the devices are made of small struts, the number of mesh elements in the boundary layer region would be considerable. This cumbersome task led to the implementation of the porous medium assumption. In this approach, the explicit geometry of the device is eliminated, and relevant porous medium assumptions are applied. Unfortunately, as it will be shown in this research, some of the porous medium approaches used in the literature are over-simplified. For example, considering the porous domain to be homogeneous is one major drawback which leads to significant errors in capturing the intra-aneurysmal flow features. Specifically, since the devices must comply with the complex geometry of an aneurysm, the homogeneity assumption is not valid.
In this research, a novel heterogeneous porous medium approach is introduced. This results in a substantial reduction in the total number of mesh elements required to discretize the flow domain while not sacrificing the accuracy of the method by over-simplifying the utilized assumptions.
ContributorsYadollahi Farsani, Hooman (Author) / Herrmann, Marcus (Thesis advisor) / Frakes, David (Thesis advisor) / Chong, Brian (Committee member) / Peet, Yulia (Committee member) / Oswald, Jay (Committee member) / Arizona State University (Publisher)
Created2018
Description
Motion estimation is a core task in computer vision and many applications utilize optical flow methods as fundamental tools to analyze motion in images and videos. Optical flow is the apparent motion of objects in image sequences that results from relative motion between the objects and the imaging perspective. Today, optical flow fields are utilized to solve problems in various areas such as object detection and tracking, interpolation, visual odometry, etc. In this dissertation, three problems from different areas of computer vision and the solutions that make use of modified optical flow methods are explained.
The contributions of this dissertation are approaches and frameworks that introduce i) a new optical flow-based interpolation method to achieve minimally divergent velocimetry data, ii) a framework that improves the accuracy of change detection algorithms in synthetic aperture radar (SAR) images, and iii) a set of new methods to integrate Proton Magnetic Resonance Spectroscopy (1HMRSI) data into threedimensional (3D) neuronavigation systems for tumor biopsies.
In the first application an optical flow-based approach for the interpolation of minimally divergent velocimetry data is proposed. The velocimetry data of incompressible fluids contain signals that describe the flow velocity. The approach uses the additional flow velocity information to guide the interpolation process towards reduced divergence in the interpolated data.
In the second application a framework that mainly consists of optical flow methods and other image processing and computer vision techniques to improve object extraction from synthetic aperture radar images is proposed. The proposed framework is used for distinguishing between actual motion and detected motion due to misregistration in SAR image sets and it can lead to more accurate and meaningful change detection and improve object extraction from a SAR datasets.
In the third application a set of new methods that aim to improve upon the current state-of-the-art in neuronavigation through the use of detailed three-dimensional (3D) 1H-MRSI data are proposed. The result is a progressive form of online MRSI-guided neuronavigation that is demonstrated through phantom validation and clinical application.
The contributions of this dissertation are approaches and frameworks that introduce i) a new optical flow-based interpolation method to achieve minimally divergent velocimetry data, ii) a framework that improves the accuracy of change detection algorithms in synthetic aperture radar (SAR) images, and iii) a set of new methods to integrate Proton Magnetic Resonance Spectroscopy (1HMRSI) data into threedimensional (3D) neuronavigation systems for tumor biopsies.
In the first application an optical flow-based approach for the interpolation of minimally divergent velocimetry data is proposed. The velocimetry data of incompressible fluids contain signals that describe the flow velocity. The approach uses the additional flow velocity information to guide the interpolation process towards reduced divergence in the interpolated data.
In the second application a framework that mainly consists of optical flow methods and other image processing and computer vision techniques to improve object extraction from synthetic aperture radar images is proposed. The proposed framework is used for distinguishing between actual motion and detected motion due to misregistration in SAR image sets and it can lead to more accurate and meaningful change detection and improve object extraction from a SAR datasets.
In the third application a set of new methods that aim to improve upon the current state-of-the-art in neuronavigation through the use of detailed three-dimensional (3D) 1H-MRSI data are proposed. The result is a progressive form of online MRSI-guided neuronavigation that is demonstrated through phantom validation and clinical application.
ContributorsKanberoglu, Berkay (Author) / Frakes, David (Thesis advisor) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2018