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The primary goal of this thesis is to improve the precision and efficiency of radiation therapy treatment. This goal is achieved by developing and implementing a direct aperture optimization (DAO) platform where the multileaf collimator (MLC) is rotated between each aperture. The approach is referred to as rotating aperture optimization (RAO). A series of tests is performed to evaluate how a final optimized plan depends on MLC parameters. Imposing constraints on the leaf sequence results in increased efficiency and a simplification of the treatment plan without compromising the quality of the dose distribution. It is also shown that an arrangement of equispaced collimator angles takes full advantage of the flexibility associated with collimator rotation. A study including ten recurring nasopharynx cancer patients is used to evaluate the capabilities of RAO compared to other optimization techniques. It is shown that RAO plans require significantly less linac radiation output (monitor units or MU) while maintaining equivalent dose distribution quality compared to plans generated with the conventional fluence based approach. Furthermore with an improved collimator rotation speed, the RAO plans should be executable in the same or less time than plans generated with the fluence-based approach. For the second part of the study it is shown that plans generated with RAO are as good as or better than plans generated with standard fixed collimator DAO. Film and ion chamber measurements indicate that RAO plans can be delivered more accurately than DAO plans. Additional applications of DAO were investigated through collaboration with two PhD students. First, Monte Carlo was used to generate pencil beam dose distributions for DAO inverse treatment planning (MC-DAO). The MC-DAO technique correctly models traditionally difficult treatment geometries such as small fields and tissue inhomogeneities. The MC-DAO also takes advantage of the improved MU efficiency associated with the DAO technique. Secondly DAO is proposed for adaptive radiation therapy. The results show that plan re-adaptation can be performed more quickly than complete plan regeneration thereby minimizing the time the patient has to spend in the treatment room and reducing the potential for geometric errors in treatment delivery. / Science, Faculty of / Physics and Astronomy, Department of / Graduate

Enhanced resistance to radiation could be caused by both chronic hypoxia and acute hypoxic which has been reported in prostate cancer in various studies. Therefore currently used dose prescriptions (70Gy in 35 fractions) for external beam radiation therapy (EBRT) of prostate cancer has been suggested insufficient to provide optimum clinical outcome. In this study, we propose a Biologically Guided Radiation Therapy approach to boost dose in hypoxic prostate tumor regions while sparing the urethra. A previously proposed hypoxia model was modified for prostate cancer and incorporated into treatment plan optimization. The concept of equivalent uniform dose (EUD) was used in the optimization and evaluation of results. CT data from 25 prostate cancer patients who recently received EBRT at the British Columbia Cancer Agency (BCCA) and hypothetical hypoxic regions manually drawn on these CT scans were selected for this study. The results show that our methods could boost dose in target volume to substantially higher levels. EUD of planning target volume increased to more than 80Gy, despite accounting for effects of hypoxia. This increase was achieved with only minor changes in dose in normal tissues, typically less than 5Gy. Notably, urethra sparing was excellent with a EUD around 64Gy. Robustness of the proposed approach is verified against various hypoxic settings. EUD comparison between RT plans in biological guided and conventional approaches using the same RT technique (Volumetric Modulated Arc Therapy) also suggests that biologically guided radiation therapy (BGRT) approach is more suitable for dose painting purposes with the advantage of delivering sufficient dose to hypoxia region in different scenarios and sparing normal tissue better. Furthermore, we also investigated the impact of inter-fraction patient set-up error and intra-fraction organ motion on the high dose gradients achieved with this proposed dose painting method and explored the feasibility of adapting geometrical uncertainties (represented as systematic error and random error) into treatment planning. Image error obtained from EPID images are used to derive systematic uncertainty and random uncertainty. During the geometrical uncertainty adapted optimization, dose matrix in PTV is shifted based on systematic error and convolved with a Gaussian kernel which is pre-calculated using random error. CT sets and organ contours from five patients who enrolled in the previous dose painting i study are selected. For each of them, seven plans are generated using cumulated uncertainty data which was collected after every five fractions. We also present the outcome in terms of equivalent uniform dose (EUD). For four of the patients, EUD history of all seven plans suggests using the proposed optimization method with uncertainty data from the first five fractions, it is possible to achieve the same target coverage of static treatment plans (difference in EUD less than 1Gy). Meanwhile, the elimination of PTV margin also leads to a significant dose reduction (more than 15Gy) in rectum. / Science, Faculty of / Physics and Astronomy, Department of / Graduate

Every year more than fourteen-thousand adults in the United States are diagnosed with glioma, the most common malignant tumor of the central nervous system. Gliomas arise from glue like glial cells and present with a range of grade and prognosis. Glioblastoma multiforme (GBM), a grade IV glioma, is the most common glioma subtype and carries dismal prognosis with fewer than one half of patients surviving one year after diagnosis. The standard treatment for GBM is resection followed by a cocktail of chemo and radiation therapy. Unfortunately, complete surgical resection is impossible for GBM, and intra-tumor heterogeneity, a GBM hallmark, negatively impacts chemo and radiation therapy efficacy. This thesis contains six chapters that evaluate advanced imaging and statistical methods that may be used to improve glioma management. Chapter one presents background information to establish the relationship of four subsequent studies with ranging topics on advanced imaging techniques, biopsy sampling, and radiomic analysis. In chapter two, a case report is presented that demonstrates the importance of advanced magnetic resonance imaging (MRI) such as arterial spin labeled (ASL) perfusion sequences. In this case, a patient with a benign cerebral lesion presents with receptive aphasia and of the imaging data acquired, only ASL showed decrease cerebral aphasia. Chapter three describes the impact biopsy selection has on correlation between prognostic and histologic features in 35 patients with GBM. Multiple biopsy selection methods were compared, resulting in a wide range in correlation significance. Chapter four presents different voxel-wise radiomic models in adult glioma patients. From one voxel-wise radiomic model, predicted disease compositions (PDC) were computed in 17 glioma patients and were able to significantly (α = 0.05) predict overall survival, tumor grade, and endothelial proliferation. Chapter five describes the feasibility and hardware constraints of simultaneous PET/MR imaging protocols. A dynamic infusion of fluorodeoxyglucose (FDG) was administered with simultaneous MR imaging including echo planar imaging (EPI) based sequences used for functional MRI (fMRI). Heat from the EPI sequences deposited in the PET detector hardware and resulted in significant hardware failure. Finally, chapter six provides outlook and application to glioma clinical management considering the methods and findings presented in each study.<br>

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<p>Loss of bone mass has long been recognized as a major factor which makes bones brittle and susceptible to fracture. Currently bone mass is measured using a dual energy photon transmission technique, and a fracture risk is derived from comparison with reference normal values. Although the risk of fracture increases as bone mass decreases, variations in trabecular bone architecture can also affect strength. Consequently, trabecular bone architecture is often cited as a factor which might contribute significantly to fracture risk. Currently, estimates of trabecular bone structure are derived from biopsy studies. Such studies are invasive, destructive, cannot be used routinely in patients ar volunteers, and certainly cannot be repeated at the same site to obtain longitudinal measurements. If routine clinical assessments of architecture are to be made, it is necessary to determine which imaging modality best reveals structure in a non-invasive manner. It is also necessary to determine how the competence of the structure can best be expressed quantitatively. This work has examined ways of assessing trabecular bone structure at the distal radius in-vivo to better understand the contribution of architecture to fracture risk. To this end, it proceeded on four major fronts. First, images of sufficient resolution were acquired using a commercial pQCT scanner and a clinical MR imager. Second, the image processing software necessary to segment the imaged trabecular structure was developed. Third, two indices were proposed to quantify the connectivity of the segmented structure. One index was derived from the application of trabecular strut analysis to a skeletonized representation of the bone network. The other quantified the marrow space by deriving a mean hole area and maximum hole area of the bone structure as it appears in two dimensions. The clinical value of these indices was tested by conducting pilot studies which examined the ability of the indices to discriminate a small group of Colles fracture patients from the normal population and to reflect normal age related changes in structure. The proposed structural parameters better discriminated Colles' fracture patients than did measures of bone mineral density. The fourth and last stage of this work examined the proportion of the variance in compressive strength of a group of radius bones that can be accounted for by bone mineral density and bone architecture. In seeking the features that were the most reliable indicators of bone strength, a combination of the mean hole area and maximum hole area had the highest correlation with peak load at fracture. This held true whether these two variables were derived from pQCT or MR images. Therefore, these structural indices may represent a potentially exciting and promising means of discriminating fracture outcomes and monitoring changes in trabecular bone structure.</p> / Thesis / Doctor of Philosophy (PhD)

In recent years, there has been much improvement in radiation therapy delivery systems used in the treatment of cancer; however, in order to fully exploit this enhancement, the computational methodology associated with radiation therapy must improve as well. It is important to accurately determine where the radiation is depositing its energy within the patient. The treatment should allow for the maximal dose at the tumor site, while minimal radiation dose to the surrounding health tissue and structures. In the Computational Reactor and Medical Physics Group here at Georgia Tech, a heterogeneous coarse-mesh transport method (COMET) has been developed for neutron transport to analyze whole-core criticality. COMET decomposes a large, heterogeneous global problem into a set of small fixed source local problems. Response functions, or rather detailed solutions, are obtained for each unique local problem. These response functions are all precomputed and stored in a library. The solution to the global solution is then bound by a linear superposition of the local problems. In this project, COMET is applied for the first time to the transport of photons in human tissues. The parameter of interest in this case is the amount of energy (dose) deposition in tissue. To determine the strengths and weaknesses of the current system, it is important to construct benchmark problems for comparison. This project will encompass a number of benchmarks. The first will involve modeling a simple two-dimensional water phantom. A second benchmark problem involves the use of a heterogeneous phantom composed of different tissues. A third benchmark problem will involve transport through slabs of aluminum, water, and lung tissue. A last, more clinically relevant benchmark problem will involve using the data from a CT scan. For each of these cases the results from COMET will be compared to the computational results obtained from EGSnrc, a Monte Carlo particle transport code. In this study, it was found that generally the results using COMET were comparable with those obtained from the Monte Carlo solutions of EGSnrc. The COMET results were also typically found thousands of times faster than the reference solution.

Radiation therapy has become a very import method for treating cancer patients. Thus, it is extremely important to accurately determine the location of energy deposition during these treatments, maximizing dose to the tumor region and minimizing it to healthy tissue. A Coarse-Mesh Transport Method (COMET) has been developed at the Georgia Institute of Technology in the Computational Reactor and Medical Physics Group for use very successfully with neutron transport to analyze whole-core criticality. COMET works by decomposing a large, heterogeneous system into a set of smaller fixed source problems. For each unique local problem that exists, a solution is obtained that we call a response function. These response functions are pre-computed and stored in a library for future use. The overall solution to the global problem can then be found by a linear superposition of these local problems. This method has now been extended to the transport of photons and electrons for use in medical physics problems to determine energy deposition from radiation therapy treatments. The main goal of this work was to develop benchmarks for testing in order to evaluate the COMET code to determine its strengths and weaknesses for these medical physics applications. For response function calculations, Legendre polynomial expansions are necessary for space, angle, polar angle, and azimuthal angle. An initial sensitivity study was done to determine the best orders for future testing. After the expansion orders were found, three simple benchmarks were tested: a water phantom, a simplified lung phantom, and a non-clinical slab phantom. Three more clinically relevant problems were developed from patient CT scans. Different coarse-mesh sizes and incident energies were tested. The COMET solutions for each case were compared to a reference solution obtained by pure Monte Carlo results from EGSnrc. In most cases, the COMET solutions produced reasonably good agreement with the COMET solutions. It was found that better results were obtained for lower energy incident photon beams as well as for larger mesh sizes. Recommendations were made for future development of COMET and the numerical benchmarks.