Optimization Problems in Radiation Therapy Treatment Planning.

Radiation therapy is one of the most common methods used to treat many types of cancer. External beam radiation therapy and the models associated with developing a treatment plan for a patient are studied. External beams of radiation are used to deliver a highly complex so-called dose distribution to a patient that is designed to kill the cancer cells while sparing healthy organs and normal tissue. Treatment planning models and optimization are used to determine the delivery machine instructions necessary to produce a desirable dose distribution. These instructions make up a treatment plan. This thesis studies four problems in radiation therapy treatment plan optimization. First, treatment planners generate a plan with a number of competing treatment plan criteria. The relationship between criteria is not known a priori. A methodology is developed for physicians and treatment planners to efficiently navigate a clinically relevant region of the Pareto frontier generated by trading off these different criteria in an informed way. Second, the machine instructions for intensity modulated radiation therapy, a common treatment modality, consist of the locations of the external beams and the non-uniform intensity profiles delivered from each of these locations. These decisions are traditionally made with separate, sequential models. These decisions are integrated into a single model and propose a heuristic solution methodology. Third, volumetric modulated arc therapy (VMAT), a treatment modality where the beam travels in a coplanar arc around the patient while continuously delivering radiation, is a popular topic among optimizers studying treatment planning due to the difficult nature of the problem and the lack of a universally accepted treatment planning method. While current solution methodologies assume a predetermined coplanar path around the patient, that assumption is relaxed and the generation of a non-coplanar path is integrated into a VMAT planning algorithm. Fourth, not all patient information is available when developing a treatment plan pre-treatment. Some information, like a patient's sensitivity to radiation, can be realized during treatment through physiological tests. Methodologies of pre-treatment planning considering adaptation to new information are studied.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113366/1/troylong_1.pd

Accurate Real Time Localization Tracking in A Clinical Environment using Bluetooth Low Energy and Deep Learning

Deep learning has started to revolutionize several different industries, and the applications of these methods in medicine are now becoming more commonplace. This study focuses on investigating the feasibility of tracking patients and clinical staff wearing Bluetooth Low Energy (BLE) tags in a radiation oncology clinic using artificial neural networks (ANNs) and convolutional neural networks (CNNs). The performance of these networks was compared to relative received signal strength indicator (RSSI) thresholding and triangulation. By utilizing temporal information, a combined CNN+ANN network was capable of correctly identifying the location of the BLE tag with an accuracy of 99.9%. It outperformed a CNN model (accuracy = 94%), a thresholding model employing majority voting (accuracy = 95%), and a triangulation classifier utilizing majority voting (accuracy = 95%). Future studies will seek to deploy this affordable real time location system in hospitals to improve clinical workflow, efficiency, and patient safety

Evolution of <i>E. coli</i> on [U^-13C] Glucose Reveals a Negligible Isotopic Influence on Metabolism and Physiology

13C-Metabolic flux analysis (13C-MFA) traditionally assumes that kinetic isotope effects from isotopically labeled compounds do not appreciably alter cellular growth or metabolism, despite indications that some biochemical reactions can be non-negligibly impacted. Here, populations of Escherichia coli were adaptively evolved for ~1000 generations on uniformly labeled 13C-glucose, a commonly used isotope for 13C-MFA. Phenotypic characterization of these evolved strains revealed ~40% increases in growth rate, with no significant difference in fitness when grown on either labeled (13C) or unlabeled (12C) glucose. The evolved strains displayed decreased biomass yields, increased glucose and oxygen uptake, and increased acetate production, mimicking what is observed after adaptive evolution on unlabeled glucose. Furthermore, full genome re-sequencing revealed that the key genetic changes underlying these phenotypic alterations were essentially the same as those acquired during adaptive evolution on unlabeled glucose. Additionally, glucose competition experiments demonstrated that the wild-type exhibits no isotopic preference for unlabeled glucose, and the evolved strains have no preference for labeled glucose. Overall, the results of this study indicate that there are no significant differences between 12C and 13C-glucose as a carbon source for E. coli growth

Integral dose investigation of non‐coplanar treatment beam geometries in radiotherapy

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134886/1/mp5055.pd

STRATEGIC AGRIBUSINESS OPERATION REALIGNMENT IN THE TEXAS PRISON SYSTEM

Mathematical programming-based systems analysis is used to examine the consequences of alternative operation configuration for the agricultural operations within the Texas Department of Criminal Justice. Continuation versus elimination of the total operation as well as individual operating departments are considered. Methodology includes a firm systems operation model combined with capital budgeting and an integer programming based investment model. Results indicate the resources realize a positive return as a whole, but some enterprises are not using resources profitably. The integer investment model is found to be superior for investigating whether to continue multiple interrelated enterprises.agribusiness, enterprise selection, mathematical programming, optimal enterprise organization, Agribusiness,

A DVH‐guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135098/1/mp5700.pd

The Influence of Climate on Flourishing and Motivational Outcomes for U.S. Masters Swimmers

The climate in which older adults exercise and participate in sport may play a role in promoting a lifetime commitment to exercising. However, little research has examined the relationship of caring (C) and task-involving (TI) climates, motivation, and well-being with respect to older adult athletes. The purpose of this study was to examine the relationship between Masters swimmers’ perceptions of the climate, effort, enjoyment, and flourishing as well as explore the mediating effects of effort and enjoyment on the relationship between climate and flourishing. U.S. Masters swimmers (n = 294; Mage = 63.57 years; 84.40% White) with 1–80 years of swimming experience (M = 34.54 years) participating in coach-led programs completed an online survey. The results of latent variable, multiple-mediator analyses via structural equation modeling revealed two important contributions to the literature: (1) when Masters swimmers perceived that they were in C and TI climates, they were more likely to report higher levels of effort and greater enjoyment and flourishing; (2) the Masters swimmers’ effort levels directly influenced their flourishing, mediating the relationship between climates and flourishing. This research has important implications for practice and policy, as U.S. Masters Swimming appears to be a fruitful avenue for promoting an enjoyable physical activity that can be experienced throughout a lifetime