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- All Subjects: Anisotropy
- Creators: Hao Hsu, Keng
Description
Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made using medical imaging such as magnetic resonance imaging (MRI) or computed tomography (CT). Treatment is informed by medical images and includes chemotherapy, radiation therapy, and surgical removal if the tumor is surgically accessible. Treatment seldom results in a significant increase in longevity, partly due to the lack of precise information regarding tumor size and location. This lack of information arises from the physical limitations of MR and CT imaging coupled with the diffusive nature of glioblastoma tumors. GBM tumor cells can migrate far beyond the visible boundaries of the tumor and will result in a recurring tumor if not killed or removed. Since medical images are the only readily available information about the tumor, we aim to improve mathematical models of tumor growth to better estimate the missing information. Particularly, we investigate the effect of random variation in tumor cell behavior (anisotropy) using stochastic parameterizations of an established proliferation-diffusion model of tumor growth. To evaluate the performance of our mathematical model, we use MR images from an animal model consisting of Murine GL261 tumors implanted in immunocompetent mice, which provides consistency in tumor initiation and location, immune response, genetic variation, and treatment. Compared to non-stochastic simulations, stochastic simulations showed improved volume accuracy when proliferation variability was high, but diffusion variability was found to only marginally affect tumor volume estimates. Neither proliferation nor diffusion variability significantly affected the spatial distribution accuracy of the simulations. While certain cases of stochastic parameterizations improved volume accuracy, they failed to significantly improve simulation accuracy overall. Both the non-stochastic and stochastic simulations failed to achieve over 75% spatial distribution accuracy, suggesting that the underlying structure of the model fails to capture one or more biological processes that affect tumor growth. Two biological features that are candidates for further investigation are angiogenesis and anisotropy resulting from differences between white and gray matter. Time-dependent proliferation and diffusion terms could be introduced to model angiogenesis, and diffusion weighed imaging (DTI) could be used to differentiate between white and gray matter, which might allow for improved estimates brain anisotropy.
ContributorsAnderies, Barrett James (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Stepien, Tracy (Committee member) / Harrington Bioengineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Material extrusion based rapid prototyping systems have been used to produceprototypes for several years. They have been quite important in the additive manufacturing field, and have gained popularity in research, development and manufacturing in a wide field of applications. There has been a lot of interest in using these technologies to produce end use parts, and Fused Deposition Modeling (FDM) has gained traction in leading the transition of rapid prototyping technologies to rapid manufacturing. But parts built with the FDM process exhibit property anisotropy. Many studies have been conducted into process optimization, material properties and even post processing of parts, but were unable to solve the strength anisotropy issue. To address this, an optical heating system has been proposed to achieve localized heating of the pre- deposition surface prior to material deposition over the heated region. This occurs in situ within the build process, and aims to increase the interface temperature to above glass transition (Tg), to trigger an increase in polymer chain diffusion, and in extension, increase the strength of the part. An increase in flexural strength by 95% at the layer interface has been observed when the optical heating method was implemented, thereby improving property isotropy of the FDM part. This approach can be designed to perform real time control of inter-filament and interlayer temperatures across the build volume of a part, and can be tuned to achieve required mechanical properties.
ContributorsKurapatti Ravi, Abinesh (Author) / Hao Hsu, Keng (Thesis advisor) / Hildreth, Owen (Committee member) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2016