Masters Theses

Surface guided radiation therapy (SGRT) is an emerging technology that uses non-ionizing methods for patient positioning and motion tracking during radiotherapy delivery. However, the use of radiotherapy boluses, which are tissue-equivalent materials placed on the skin to increase surface dose, has been shown to interfere with SGRT systems due to reflections from the bolus surface. This thesis presents the development and validation of an opaque and non-glossy radiotherapy bolus called the "Surface Guidance Optimized" (SGO), which is a variation of the previously developed transparent Clearsight bolus.The Surface Guidance Optimized bolus was rendered opaque by adding 0.6% titanium dioxide and given a matte finish using matte release paper. Spectroscopy measurements confirmed optimal opaqueness, while gloss meter readings verified a non-glossy surface. The bolus density was quantified to be 0.853 g/cm3 using water displacement and CT methods. Dosimetric characterization through direct surface dose measurements and Monte Carlo simulations demonstrated the SGO bolus mimics the dose deposition of water-equivalent materials when accounting for density differences. Compatibility testing with the AlignRT SGRT system showed the bolus allowed accurate surface reconstruction and submillimeter tracking (within 0.4 mm) under different lighting conditions. Overall, the SGO bolus mitigates issues of transparency and glossiness that interfered with SGRT systems, while maintaining desirable dosimetric properties for clinical use as a radiotherapy bolus compatible with modern surface guided techniques.

This paper seeks to contribute to the study of early postwar Japanese women’s history by focusing on rural women, a group that has received relatively less attention in recent scholarship. It aims to understand the changes in the lives and worldviews of Japanese farm women from 1945 to 1950 as shaped by the ambitious initiatives of the Supreme Commander for the Allied Powers (SCAP), the Japanese government, and the local reception and internalization of new ideologies. Through the lens of women-and-lifestyle-related content in Ie no hikari 家の光 (Light of the Home), the most influential rural family magazine in prewar and postwar Japan, this paper intends to explore how the magazine tailored official campaigns to the rural context with the help of local activists and farm women themselves, leaving both tangible and intangible impacts on the daily lives of women and their families. It also investigates the various ways in which local women responded to and interacted with the official new life campaigns that promised them concrete improvements in material lives and social status. As the magazine served as a middle ground where top-down initiatives intersected with local efforts to internalize official languages in the late 1940s, it also provides access to the local voices of farm women at the time. These precious voices, however limited, allow us to better situate rural women within the tabulating social milieu of early postwar Japan and to delve deeper into their daily lives.

This thesis presents a comprehensive study on nonparametric Bayesian density estimation using Gaussian processes (GP). We explore the logistic Gaussian Process (LGP) and introduce an innovative approach termed the tree-logistic-link Gaussian process (TLLGP). This method aims to improve computational efficiency while maintaining modeling flexibility. We address the computational challenges traditionally associated with LGP by implementing a novel tree-based strategy, thereby reducing the complexity of posterior computations. Through a series of numerical experiments, we demonstrate the effectiveness of TLLGP in various scenarios, comparing its performance with other methods. The results highlight the advantages of our approach in terms of computational speed and accuracy in density estimation tasks. This work contributes to the fields of Bayesian statistics and machine learning by providing a more efficient tool for density estimation, especially beneficial for large high-dimensional data where traditional methods fall short due to their computational demands.

Introduction: The current head-and-neck (HN) fluence map generator tends to producehighly modulated fluence maps and therefore high monitor units (MUs) for each beam, which leads to more delivery uncertainty and leakage dose. This project implements diffu- sion into the training process and modifies the loss functions to mitigate this effect.

Methods: The dataset consists of 200 head-and-neck (HN) patients receiving intensity mod-ulated radiation therapy (IMRT) for training, 16 for validation, and 15 for testing. Two models were trained, one with-diffusion and one without. The original model was a con- ditional generative adversarial network (GAN) written in TensorFlow, the model without diffusion was written to be the PyTorch equivalent of the original model. After confirming the model was properly converted to PyTorch by comparing outputs, both new models were modified to use binary cross entropy for the GAN loss and mean absolute error as a third loss function for the generator. Hyperparameters were carefully selected based on the training script for the original model, and further tuned with trial and error. The diffusion was implemented based on Diffusion-GAN and the associated GitHub repository. The two new models were compared by plotting training loss vs epoch over 500 epochs. The two models were compared to the original model by comparing the output fluence maps to the ground truth using similarity index and comparing DVH statistics among the three models.

Results: The with-diffusion model and no-diffusion model achieved similar training loss.The diffusion model and no-diffusion model consistently delivered better parotid sparing than the original model and delivered less dose to four of the six tested OAR. The with- diffusion model delivered less dose to five of the six tested OAR. The diffusion model had the least MUs: 23% less than the original model and 3% less than the no-diffusion model. The diffusion model had lower D2cc: 4% less than the original model and 1% less than the no-diffusion model on average. All three plans deliver 95% of the prescription dose to nearly the same percentage of PTV volume.

Conclusion: Implementing diffusion does not provide a significant impact on training timeand training loss. However, it does enable comparable dose performance to both the no- diffusion and original models, while significantly reducing the total MU’s and 3D max 2cc relative to the original model and slightly reducing these metrics relative to the no-diffusion model, indicating smoother fluence modulation. In addition, both new models reduced dose to the right and left parotids relative to the original model, and to four of six tested OAR total, while the with-diffusion model consistently delivers less dose to OAR than the no- diffusion model. This indicates that both the new loss functions and diffusion reduce the overall dose to the OARs while preserving dose conformity around the target.

Purpose: Currently, the Magnetization Prepared Rapid Gradient Echo (MPRAGE) Magnetic Resonance Imaging (MRI) sequence is frequently used for brain tissue segmentation in the clinic due to its high image contrast. However, one of the limitations of the MPRAGE sequence lies in its susceptibility to metal artifacts, while the Turbo Spin Echo (TSE) sequences, can resist metal artifacts. Previous studies have shown that for patients with metal implants, metal-artifact-reduced MPRAGE images can be generated from TSE images. Conventional brain segmentation methods on MPRAGE images, such as FreeSurfer, are time-consuming. Therefore, the purpose of this study was to investigate a fast brain segmentation method via deep learning-based frameworks for patients with metal implants, using TSE images as input.Materials and Methods: A dataset consisting of 369 patients in total was used. Each patient contained 160 two-dimensional slices of T1-weighted (T1WI), T2-weighted (T2WI), and PD-weighted (PDWI) TSE brain MR images, respectively. The matrix size of the original images was 240 × 240. Two types of MPRAGE as intermediate steps were synthesized from T1WI, T2WI, and PDWI using mathematical calculations or Conditional Generative Adversarial Network (cGAN) algorithms. FreeSurfer software was used to generate brain segmentations on the MPRAGE, which were considered as the ground truth for deep-learning network training and eventual evaluation. Two research aims were investigated. Aim 1 was to utilize three-channel TSE images (T1WI, T2WI, and PDWI) to first mathematically synthesize MPRAGE images, and then perform segmentation via deep learning-based models. Aim 2 was to use single-channel TSE images as input directly or indirectly to achieve brain segmentation using deep learning-based models. Both UNet and UNet++ models were examined. The Dice coefficient was used to evaluate the performance of the above-mentioned segmentation aims. Results: For Aim 1, the Dice coefficient between the ground truth and the cortex segmentations generated by the UNet++ network using three-channel TSE images as original input and mathematically synthesized MPRAGE as direct input was 0.919 ± 0.03. For Aim 2, the Dice coefficient between the ground truth and the cortex segmentations generated by the UNet network using single-channel TSE images directly as input was 0.602 ± 0.06. The Dice coefficient between the ground truth and the cortex segmentations generated by the single-channel TSE images as original input and cGAN-synthesized MPRAGE as direct input using the UNet++ network was 0.766 ± 0.07. Conclusion: Two aims using three-channel or single-channel TSE images as original input and brain segmentation as output were investigated in this study. Three-channel TSE images as original input, and mathematically synthesized MPRAGE as direct input to the UNet++ network showed superior results. Single-channel TSE images as original input and cGAN-synthesized MPRAGE as direct input to the UNet++ network showed relatively lower performance. Further research is warranted to improve the performance of single-channel TSE-based deep-learning segmentation methods. Keywords: UNet++, MRI, Brain Image, Segmentation, TSE, MPRAGE

AbstractPurpose: DW-MRI and their derived apparent diffusion coefficient (ADC) maps have been shown to be beneficial in the diagnosis and treatment of various cancer types. This work determines the potential role of DW-MRI and ADC maps in GTV delineation for gynecological cancer patients undergoing external beam radiation therapy (EBRT) and brachytherapy. Our study also looked at the longitudinal changes in DWI/ADC values during the course of external beam treatments, as well as during the five brachytherapy fractions. Methods: The first aspect of this study involved validating the console-derived DW image sets and ADC maps using an in-house Matlab code designed for this purpose. Next, the b-value, which describes the sensitivity of the imaging sequence to diffusion, was optimized through a quantitative and qualitative analysis. The quantitative analysis involved maximizing the contrast-to-noise ratios between the tumor and various structures, including the endocervical canal, endometrium, myometrium, and gluteal subcutaneous fat. The qualitative analysis had two radiation oncologists ranking different DWI sets at various b-values based on tumor conspicuity and total image quality on a scale of 1-5, 1 being the best and 5 being the worst. After determining the optimal b-value for DW image calculation, an analysis of GTV contouring was performed in Medical Image Merge (MIM). This involved a radiation oncologist contouring GTVs on three image sets; axial T2 MRI, axial T2 MRI fused with DWI at b=1300 s/mm2, and axial T2 MRI fused with ADC at b=0, 1000 s/mm2. This was done for 16 patients, 5 of whom had pre-EBRT and pre-brachytherapy scans and 11 of whom had only pre-brachytherapy scans. The contours between the three sets were compared on each scan using the Hausdorff distance, Jaccard index, DICE coefficient, and mean pixel value, all of which were calculated in MIM. The final portion of this study was a longitudinal look at the CTVHRs throughout the course of brachytherapy. The CTVHRs were analyzed on the axial T2 MRI, DWI, and ADC maps. Results: The contrast-to-noise ratios of the endocervical canal, endometrium, myometrium, and gluteal subcutaneous fat all compared to tumor were optimized at either b=1300, 1600, or 1800 s/mm2. DW images at b=1300s/mm2 were ranked the best by both physicians in terms of total image quality and tumor conspicuity for the qualitative analysis for b-value optimization. For GTN analysis, the volumes of the GTVs contoured with the help of the DW images and with the help of the ADC maps were not significantly different (p=0.23404). The Dice coefficients, Hausdorff distances, and Jaccard indices calculated with respect to the reference GTVs were not significantly different between the GTVs contoured with the help of the DW images and the GTVs contoured with the help of the ADC maps. The p-values were 0.84148, 0.56868, and 0.95216, respectively. The volumes of the reference GTVs compared to the volumes of the GTVs contoured with the help of DWI and ADC maps were not statistically significant with p-values of 0.6672 and 0.42372, respectively. The mean pixel values in the reference GTVs compared to the mean pixel values in the GTVs contoured with the help of DWI and ADC maps were not statistically significant with p-values of 0.17384 and 0.68916. The mean pixel values within the GTV contoured with the help of DWI were almost significantly lower than the mean pixel values within the GTV contoured with the help of the ADC maps (p=0.0536). Looking to artifact quantification, no significant artifacts were seen in the axial T2 MRI, DWI, or ADC map outside of the tandem contour in the ice water phantom experiment. In the longitudinal analysis of the CTVHRs, the percent difference between the largest and smallest average of the mean pixel values in Figure 7 is 17.4% with no apparent trend along fractions. The percent difference between the largest and smallest average of the mean pixel values in Figure 8 and Figure 9 are 27.3% and 6.5%, respectively. There is no apparent trend for the average of the mean pixel values when looking at the ADC maps, but the average of mean pixel values decreases throughout the brachytherapy fractions when looking at the DW images. The average standard deviation of the pixel values within each CTVHR on the axial T2 MR and DW image sets generally decreases throughout the course of brachytherapy, while the average standard deviation on ADC generally increases along fractions. However, the percent difference between the largest and smallest average standard deviation on the axial T2 MR image set, the DW image set, and the ADC maps is 18.5%, 43.4%, and 4.9%, respectively. Therefore, while the standard deviation on the ADC maps is generally increasing, it is to a small extent. Conclusion: The results of this study indicate the potential of using ADC maps in tandem with axial T2 MRI to increase the accuracy of GTV delineation in cervical cancer patients undergoing EBRT and brachytherapy. However, a larger sample size is needed to provide more insight into their use during the contouring workflow.

Due to a high burden of disease of pneumonia in Vietnam, the country not including the pneumococcal conjugate vaccine (PCV) in its National Expanded Programme of Immunization (EPI), and the scarce data on PCV vaccine coverage or caregivers’ behavior within the country, it is imperative to assess the Knowledge, Attitude and Practice (KAP) of the caregivers’ community, to further explore ways to increase PCV uptake. The purpose of this study is to understand the KAP of caregivers towards PCV inoculation for children in Hanoi, VietnamMethodology: 338 respondents fulfilled the Qualtrics questionnaire and 26 respondents (16 caregivers and 10 health workers) were interviewed in Hanoi, Vietnam, using semi-structured interviews in June-December 2023. Materials and data were transcribed between Vietnamese and English, and analyzed according to selected themes. Discussion/Conclusions: Although the findings suggest that caregivers in Hanoi have limited knowledge on PCV, support for attitude and practice on accepting PCV exists, especially from caregivers with high socio-economic status. This study wished to contribute to a better understanding of the KAP factors regarding childhood vaccines, which may support decision-making about vaccine policies, and be utilized for creating suitable vaccine promotion materials for child caregivers.

As the twenty-first century has entered an era of catastrophes, post-catastrophic trauma writings in world literature bear witness and give testimonies to the moments of crisis. With a comparative literary study of the post-catastrophic trauma writings and other forms of representations that respond to the 9/11 terrorism in the United States and the Covid-19 pandemic outbreaks in China, this research explores the question of how the collective traumas develop dynamic relationships with individuality and influence individuals’ mental lives affectively. In the catastrophic aftermath, the collective traumas shared by the individuals act on their interiority and form a sense of collective isolation, which means that an individual staying in a collectivity remains unconsciously isolated affects. The research will illustrate the embodiments of collective isolation at an individual level and delve into its social causes at a collective level. On an individual level, collective isolation is recognized as a traumatized subject’s sense of detachment from the chronological present, showing a dislocation with time. On a collective level, collective isolation is an exteriorization of a traumatized society by two types of violence: subjective violence and objective violence. The intensive conflicts around subjective violence directly by catastrophes may transform into invisible objective violence, which constantly and implicitly influences politics, cultures, and human affects. This research would land at the point that collectivity and individuality as two spatial concepts could be interpenetrated through affects, illustrating that the collective traumas represent dynamic relationships among violence, affects, public spheres, and the individual’s mental world.

Background: Cervical cancer is a common gynecological malignancy among women worldwide. Among the primary modalities for treating cervical cancer, radiation therapy occupies a central role. Using Cone-Beam Computed Tomography (CBCT) scans obtained prior to treatment for target registration and alignment holds critical significance for precision radiation therapy. Accurately contouring targets and critical-organs-at risk (OARs) is the most time-consuming task for radiation oncologists. The OAR contouring in CBCT plays a crucial role in the radiotherapy of cervical cancer. Specifically, the location and volume of the rectum and bladder can significantly impact the precision of cervical cancer treatment, as the patients need to drink certain amount of water to fill the bladder prior to the treatment for target localization. The resulting change in position of rectum and bladder may lead to alterations in the target dose. Further, changes in radiation dose to these two OARs can directly affect the severity of the acute and late radiation induced damage. Therefore, the OAR contouring not only allows for better localization before each radiotherapy session, but also provides valuable reference for clinicians when they need to adjust the treatment plan.Purpose: The objective of this study is to evaluate the capabilities of four deep-learning models for contouring OARs in CBCT images of cervical cancer patients. Materials and Methods: The study dataset comprising 40 sets of CBCT images were collected from the Fujian Provincial Cancer Hospital in China. Two experienced radiation oncologists meticulously delineated 10 groups of OARs (Body, Bladder, Bone Marrow, Bowel Bag, Femoral Head L, Femoral Head R, Femoral Head and Neck L, Femoral Head and Neck R, Rectum, Spinal Canal) on the CBCT images as reference/ground truth. Subsequently, the 24 sets of CBCT reference were used to train the CBCT model, and the unedited CBCT images of the remaining 16 sets were used for comparing with their reference to test the four models. The only difference between these four models is the adoption of different neural network structures. They are classic U-Net, Flex U-Net, Attention U-Net (ATT), and SegResNet respectively. The evaluation of contouring quality for the four models was performed using the metrics such as 95 percentile Hausdorff Distance (HD95), Dice Similarity Coefficient (DICE), Average Symmetric Surface Distance (ASSD), Maximum Symmetric Surface Distance (MSSD), and Relative Absolute Volume Difference (RAVD), respectively. Results: The average DICE was 0.86 for bladder contouring among four models. The average DICE for rectum on CBCT image was 0.84 for four models. Conclusion: According to the quantitative analysis, classic U-Net neural network architecture with minor adjustments can obtain competitive segmentation on CBCT images.

Objective: This dissertation investigates therapists' perceptions and expectations of technology application in non-pharmaceutical therapies (NPT) for individuals with Mild Cognitive Impairment (MCI) and Dementia, emphasizing the role of Information and Communication Technology (ICT).

Methods: Adopting a qualitative research framework, this study utilizes methodology comprising semi-structured interviews, and participatory observations. Semi-structured interviews employed convenience sampling to engage experienced therapists in in-depth discussions, while participatory observations offered a firsthand examination of therapeutic settings and methodologies, including music therapy, and reminiscence therapy.

Results: Integrating insights from therapist interviews, and immersive participatory observations, the study elucidates the ambivalent nature of ICT’s role in NPT for treating dementia patients. Challenges identified include older patients’ resistance to new technologies and the difficulty in quantifying the therapeutic outcomes of ICT applications. Despite these hurdles, therapists exhibit a collective optimism toward the potential of rapidly evolving technology to enhance the overall efficacy of NPT in the recovery processes for dementia patients.

Conclusion: The research underscores a complex landscape where the integration of ICT in NPT presents both opportunities and challenges. Therapists’ hopeful outlook signals a broader consensus on the potential transformative impact of technology in dementia intervention, suggesting a need for further innovation and research in this domain. This study contributes to the dialogue on integrating ICT in therapeutic practices, offering a nuanced understanding of its implications for enhancing dementia intervention.

Background: Population aging and rural urbanization were two major trends in China. Past researches had shown that relocation and displacement could have a negative impact on the mental health of senior adults. Land expropriation and increasing rural migration due to China's urbanization process had created a growing but understudied group of "landless farmers." This study explored the impact of relocation from rural villages urban resettlement on the mental health of older adults in China in terms of depression.Method: The mixed-method study collected survey data from 219 adults aged ≥60 years in one relocated village (Zhangjia) and one non-relocated village (Xicheng) in Jinhua City, Zhejiang Province, China. Mental health measures included the Geriatric Depression Scale. Semi-structured interviews with 10 relocated older residents provided qualitative data. Quantitative analyses examined differences in social networks, amenities, and levels of depression between groups. Logistic regression analyzed predictors of depressive symptoms. Qualitative data were analyzed using thematic analysis. Result: No significant difference in depression was found between the two communities, but the social network scores of older adults in the resettlement community were significantly lower than those in the original village. Poor living facilities were associated with a higher rate of depression in both communities. In addition, a good social network was an important protective factor against depression in the relocated population. Qualitative findings revealed feelings of boredom, reduced social interaction, and changes in family relationships following the move. In summary, quantitative and qualitative data suggested that the disruption of living habits and isolation caused by relocation may have a negative impact on the mental health of older adults in rural China. Discussion: Quantitative and qualitative data suggested that the disruption of living habits and isolation caused by relocation may have a negative impact on the mental health of older adults in rural China. Conclusion: The study emphasized the need for targeted interventions to support mental well-being in this vulnerable population undergoing relocation.

AbstractIntroduction：This study evaluates a new proton therapy filter designed to eliminate the need for energy adjustments. Utilizing the machine's maximum energy, the filter ensures sufficient tumor coverage through the Bragg-peak, potentially improving treatment efficiency by shortening delivery time. Methods：Implemented on the matRad platform, each plan utilized a single arc composed of 72 beams, each spaced 5 degrees apart. Open-access datasets, including TG-119 C-shape, a prostate case, and a liver case, were employed. The prescribed doses for these cases were 50Gy in 25 fractions, 68Gy in 34 fractions, and 45Gy in 25 fractions, respectively. Simplifying from multiple energy layers to a single energy layer for each beam can reduce treatment delivery time. Maintaining spot coverage with a single energy layer for each beam is a critical optimization aspect. The spot coverage, P(i,j), is optimized to maximize spot coverage and the optimization is called mono energy optimization. However, considering spot coverage alone is insufficient; the energy level must also be considered. Higher energy levels indicate a thinner range shifter, which reduces scatter and attenuation caused by range shifters. The new optimization process, called higher mono energy optimization, gave priority to deeper layers and larger spot sizes, using a function that normalizes input energy and combines it with alpha and beta coefficients to optimize the energy function E(i,j) and spot coverage P(i,j). The optimal energy layers were selected, and the initial beam energy was set at 236MeV. All beamlet was adjusted to specific energy levels with a custom-designed PMMA filter based on stopping power, facilitating a smooth transition to the desired energy levels. The effectiveness of this approach was evaluated by comparing dose metrics with those from the Intensity Modulated Proton Therapy (IMPT) method using two or three beams. Results: PTV coverages were relatively close between the IMPT and range filter plans. Organs at Risk (OAR) experienced a dose increase due to enhanced scattering. Simulated treatment delivery times for the three tested range filter plans demonstrated the efficiency, with prostate at 360s, liver at 340s, and TG119 at 390s. Conclusions: Mono-energy with range filters proton therapy is a feasible approach for expediting treatment delivery without compromising the quality of the treatment plan.