Matching Items (115)
Filtering by
- Status: Published
Description
Magnetic Resonance Imaging (MRI) is limited in speed and resolution by the inherently low Signal to Noise Ratio (SNR) of the underlying signal. Advances in sampling efficiency are required to support future improvements in scan time and resolution. SNR efficiency is improved by sampling data for a larger proportion of total imaging time. This is challenging as these acquisitions are typically subject to artifacts such as blurring and distortions. The current work proposes a set of tools to help with the creation of different types of SNR efficient scans. An SNR efficient pulse sequence providing diffusion imaging data with full brain coverage and minimal distortion is first introduced. The proposed method acquires single-shot, low resolution image slabs which are then combined to reconstruct the full volume. An iterative deblurring algorithm allowing the lengthening of spiral SPoiled GRadient echo (SPGR) acquisition windows in the presence of rapidly varying off-resonance fields is then presented. Finally, an efficient and practical way of collecting 3D reformatted data is proposed. This method constitutes a good tradeoff between 2D and 3D neuroimaging in terms of scan time and data presentation. These schemes increased the SNR efficiency of currently existing methods and constitute key enablers for the development of SNR efficient MRI.
ContributorsAboussouan, Eric (Author) / Frakes, David (Thesis advisor) / Pipe, James (Thesis advisor) / Debbins, Joseph (Committee member) / Towe, Bruce (Committee member) / Arizona State University (Publisher)
Created2011
Description
Current treatment methods for cerebral aneurysms are providing life-saving measures for patients suffering from these blood vessel wall protrusions; however, the drawbacks present unfortunate circumstances in the invasive procedure or with efficient occlusion of the aneurysms. With the advancement of medical devices, liquid-to-solid gelling materials that could be delivered endovascularly have gained interest. The development of these systems stems from the need to circumvent surgical methods and the requirement for improved occlusion of aneurysms to prevent recanalization and potential complications. The work presented herein reports on a liquid-to-solid gelling material, which undergoes gelation via dual mechanisms. Using a temperature-responsive polymer, poly(N-isopropylacrylamide) (poly(NIPAAm), the gelling system can transition from a solution at low temperatures to a gel at body temperature (physical gelation). Additionally, by conjugating reactive functional groups onto the polymers, covalent cross-links can be formed via chemical reaction between the two moieties (chemical gelation). The advantage of this gelling system comprises of its water-based properties as well as the ability of the physical and chemical gelation to occur within physiological conditions. By developing the polymer gelling system in a ground-up approach via synthesis, its added benefit is the capability of modifying the properties of the system as needed for particular applications, in this case for embolization of cerebral aneurysms. The studies provided in this doctoral work highlight the synthesis, characterization and testing of these polymer gelling systems for occlusion of aneurysms. Conducted experiments include thermal, mechanical, structural and chemical characterization, as well as analysis of swelling, degradation, kinetics, cytotoxicity, in vitro glass models and in vivo swine study. Data on thermoresponsive poly(NIPAAm) indicated that the phase transition it undertakes comes as a result of the polymer chains associating as temperature is increased. Poly(NIPAAm) was functionalized with thiols and vinyls to provide for added chemical cross-linking. By combining both modes of gelation, physical and chemical, a gel with reduced creep flow and increased strength was developed. Being waterborne, the gels demonstrated excellent biocompatibility and were easily delivered via catheters and injected within aneurysms, without undergoing degradation. The dual gelling polymer systems demonstrated potential in use as embolic agents for cerebral aneurysm embolization.
ContributorsBearat, Hanin H (Author) / Vernon, Brent L (Thesis advisor) / Frakes, David (Committee member) / Massia, Stephen (Committee member) / Pauken, Christine (Committee member) / Preul, Mark (Committee member) / Solis, Francisco (Committee member) / Arizona State University (Publisher)
Created2012
Description
There is a growing interest for improved high-accuracy camera calibration methods due to the increasing demand for 3D visual media in commercial markets. Camera calibration is used widely in the fields of computer vision, robotics and 3D reconstruction. Camera calibration is the first step for extracting 3D data from a 2D image. It plays a crucial role in computer vision and 3D reconstruction due to the fact that the accuracy of the reconstruction and 3D coordinate determination relies on the accuracy of the camera calibration to a great extent. This thesis presents a novel camera calibration method using a circular calibration pattern. The disadvantages and issues with existing state-of-the-art methods are discussed and are overcome in this work. The implemented system consists of techniques of local adaptive segmentation, ellipse fitting, projection and optimization. Simulation results are presented to illustrate the performance of the proposed scheme. These results show that the proposed method reduces the error as compared to the state-of-the-art for high-resolution images, and that the proposed scheme is more robust to blur in the imaged calibration pattern.
ContributorsPrakash, Charan Dudda (Author) / Karam, Lina J (Thesis advisor) / Frakes, David (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2012
Description
Single cell analysis has become increasingly important in understanding disease onset, progression, treatment and prognosis, especially when applied to cancer where cellular responses are highly heterogeneous. Through the advent of single cell computerized tomography (Cell-CT), researchers and clinicians now have the ability to obtain high resolution three-dimensional (3D) reconstructions of single cells. Yet to date, no live-cell compatible version of the technology exists. In this thesis, a microfluidic chip with the ability to rotate live single cells in hydrodynamic microvortices about an axis parallel to the optical focal plane has been demonstrated. The chip utilizes a novel 3D microchamber design arranged beneath a main channel creating flow detachment into the chamber, producing recirculating flow conditions. Single cells are flowed through the main channel, held in the center of the microvortex by an optical trap, and rotated by the forces induced by the recirculating fluid flow. Computational fluid dynamics (CFD) was employed to optimize the geometry of the microchamber. Two methods for the fabrication of the 3D microchamber were devised: anisotropic etching of silicon and backside diffuser photolithography (BDPL). First, the optimization of the silicon etching conditions was demonstrated through design of experiment (DOE). In addition, a non-conventional method of soft-lithography was demonstrated which incorporates the use of two positive molds, one of the main channel and the other of the microchambers, compressed together during replication to produce a single ultra-thin (<200 µm) negative used for device assembly. Second, methods for using thick negative photoresists such as SU-8 with BDPL have been developed which include a new simple and effective method for promoting the adhesion of SU-8 to glass. An assembly method that bonds two individual ultra-thin (<100 µm) replications of the channel and the microfeatures has also been demonstrated. Finally, a pressure driven pumping system with nanoliter per minute flow rate regulation, sub-second response times, and < 3% flow variability has been designed and characterized. The fabrication and assembly of this device is inexpensive and utilizes simple variants of conventional microfluidic fabrication techniques, making it easily accessible to the single cell analysis community.
ContributorsMyers, Jakrey R (Author) / Meldrum, Deirdre (Thesis advisor) / Johnson, Roger (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2012
Description
Diabetic retinopathy (DR) is a common cause of blindness occurring due to prolonged presence of diabetes. The risk of developing DR or having the disease progress is increasing over time. Despite advances in diabetes care over the years, DR remains a vision-threatening complication and one of the leading causes of blindness among American adults. Recent studies have shown that diagnosis based on digital retinal imaging has potential benefits over traditional face-to-face evaluation. Yet there is a dearth of computer-based systems that can match the level of performance achieved by ophthalmologists. This thesis takes a fresh perspective in developing a computer-based system aimed at improving diagnosis of DR images. These images are categorized into three classes according to their severity level. The proposed approach explores effective methods to classify new images and retrieve clinically-relevant images from a database with prior diagnosis information associated with them. Retrieval provides a novel way to utilize the vast knowledge in the archives of previously-diagnosed DR images and thereby improve a clinician's performance while classification can safely reduce the burden on DR screening programs and possibly achieve higher detection accuracy than human experts. To solve the three-class retrieval and classification problem, the approach uses a multi-class multiple-instance medical image retrieval framework that makes use of spectrally tuned color correlogram and steerable Gaussian filter response features. The results show better retrieval and classification performances than prior-art methods and are also observed to be of clinical and visual relevance.
ContributorsChandakkar, Parag Shridhar (Author) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2012
Description
Recent advances in camera architectures and associated mathematical representations now enable compressive acquisition of images and videos at low data-rates. While most computer vision applications of today are composed of conventional cameras, which collect a large amount redundant data and power hungry embedded systems, which compress the collected data for further processing, compressive cameras offer the advantage of direct acquisition of data in compressed domain and hence readily promise to find applicability in computer vision, particularly in environments hampered by limited communication bandwidths. However, despite the significant progress in theory and methods of compressive sensing, little headway has been made in developing systems for such applications by exploiting the merits of compressive sensing. In such a setting, we consider the problem of activity recognition, which is an important inference problem in many security and surveillance applications. Since all successful activity recognition systems involve detection of human, followed by recognition, a potential fully functioning system motivated by compressive camera would involve the tracking of human, which requires the reconstruction of atleast the initial few frames to detect the human. Once the human is tracked, the recognition part of the system requires only the features to be extracted from the tracked sequences, which can be the reconstructed images or the compressed measurements of such sequences. However, it is desirable in resource constrained environments that these features be extracted from the compressive measurements without reconstruction. Motivated by this, in this thesis, we propose a framework for understanding activities as a non-linear dynamical system, and propose a robust, generalizable feature that can be extracted directly from the compressed measurements without reconstructing the original video frames. The proposed feature is termed recurrence texture and is motivated from recurrence analysis of non-linear dynamical systems. We show that it is possible to obtain discriminative features directly from the compressed stream and show its utility in recognition of activities at very low data rates.
ContributorsKulkarni, Kuldeep Sharad (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2012
Description
In this study, the specific goal was to evaluate the effectiveness of utilizing a novel virtual reality software package with a haptic device to practice spine surgery. This spine surgery simulator was commissioned by Barrow Neurological Institute (BNI) and is as yet untested. To test the simulator, an experiment was run in which resident neurosurgeons at Barrow Neurological Institute were asked to perform two “virtual surgeries” with the spine surgical simulator, provide observations on the simulator, and then complete a questionnaire evaluating different aspects of the simulator. The mean questionnaire score across all the neurosurgical residents was found to be 65.5 % ± 9.4 % of the maximum score which suggests that certain aspects of the virtual spine surgical simulator were deemed to be effective by the resident neurosurgeons but that improvements need to be made for the simulator to be fully ready as a teaching and planning tool. As of right now, the simulator is more suited as a training tool instead of a planning tool. Improvements that should be implemented include changing the hardware placement of the haptic device and the computer, minimizing aberrant tactile feedback, and adding anatomical and planning detail to the software to provide a more accurate reflection of spine surgery. It was also suggested that future experiments that evaluate an improved simulator should ensure that participants are trained adequately and have enough time to complete surgical operations to get a fair assessment of the tool.
ContributorsIyer, Sudarshan Rajan (Author) / Frakes, David (Thesis director) / Crawford, Neil (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2015-05
Description
A specific type of Congenital Heart Defect (CHD) known as Coarctation (narrowing) of the Aorta (CoA) prevails in 10% of all CHD patients resulting in life-threatening conditions. Treatments involve limited medical therapy (i.e PGE1 therapy), but in majority of CoA cases, planned surgical treatments are very common. The surgical approach is dictated by the severity of the coarctation, by which the method of treatments is divided between minimally invasive and extensive invasive procedures. Modern diagnostic procedures allude to many disadvantages making it difficult for clinical practices to properly deliver an optimal form of care. Computational Fluid Dynamics (CFD) technique addresses these issues by providing new forms of diagnostic measures that is non-invasive, inexpensive, and more accurate compared to other evaluative devices. To explore further using the CFD based alternative diagnostic measure, this project aims to validate CFD techniques through in vitro studies that capture the fluid flow in anatomically accurate aortic structures. These studies combine particle image velocimetry and catheterization experimental techniques in order to provide a significant knowledge towards validation of fluid flow simulations.
ContributorsPathangey, Girish (Co-author) / Matheny, Chris (Co-author) / Frakes, David (Thesis director) / Pophal, Stephen (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2015-05
Description
This study investigates the application of Computational Fluid Dynamics (CFD) to the medical field. An overview of recent advances in computational simulation and modeling in medical applications is provided, with a particular emphasis on CFD. This study attempts to validate CFD and demonstrate the possibility for applying CFD to the clinical treatment and evaluation of atherosclerotic disease. Three different geometric configurations are investigated: one idealized bifurcation with a primary diameter of 8 mm, and two different patient-specific models of the bifurcation from the common femoral artery to the superficial and deep femoral arteries. CFD is compared against experimental measurements of steady state and pulsatile flow acquired with Particle Image Velocimetry (PIV). Steady state and pulsatile flow rates that are consistent with those observed in the femoral artery are used. In addition, pulsatile CFD simulations are analyzed in order to demonstrate meaningful clinical applications for studying and evaluating the treatment of atherosclerotic disease. CFD was successfully validated for steady state flow, with an average percent error of 6.991%. Potential for validation was also demonstrated for pulsatile flow, but methodological errors warrant further investigation to reformulate methods and analyze results. Quantities frequently associated with atherosclerotic disease and arterial bifurcations, such as large variations in wall shear stress and the presence of recirculation zones are demonstrated from the pulsatile CFD simulations. Further study is required in order to evaluate whether or not such phenomena are represented by CFD accurately. Further study must also be performed in order to evaluate the practicality and utility of CFD for the evaluation of atherosclerotic disease treatment.
ContributorsMortensen, Matthew James (Author) / VanAuker, Michael (Thesis director) / Frakes, David (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Glioblastoma Multiforme (GBM) is an aggressive and deadly form of brain cancer with a median survival time of about a year with treatment. Due to the aggressive nature of these tumors and the tendency of gliomas to follow white matter tracks in the brain, each tumor mass has a unique growth pattern. Consequently it is difficult for neurosurgeons to anticipate where the tumor will spread in the brain, making treatment planning difficult. Archival patient data including MRI scans depicting the progress of tumors have been helpful in developing a model to predict Glioblastoma proliferation, but limited scans per patient make the tumor growth rate difficult to determine. Furthermore, patient treatment between scan points can significantly compound the challenge of accurately predicting the tumor growth. A partnership with Barrow Neurological Institute has allowed murine studies to be conducted in order to closely observe tumor growth and potentially improve the current model to more closely resemble intermittent stages of GBM growth without treatment effects.
ContributorsSnyder, Lena Haley (Author) / Kostelich, Eric (Thesis director) / Frakes, David (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05