A cloud-based system for measuring radiation treatment plan similarity

PURPOSE: Radiation therapy is used to treat cancer using carefully designed plans that maximize the radiation dose delivered to the target and minimize damage to healthy tissue, with the dose administered over multiple occasions. Creating treatment plans is a laborious process and presents an obstacle to more frequent replanning, which remains an unsolved problem. However, in between new plans being created, the patient's anatomy can change due to multiple factors including reduction in tumor size and loss of weight, which results in poorer patient outcomes. Cloud computing is a newer technology that is slowly being used for medical applications with promising results. The objective of this work was to design and build a system that could analyze a database of previously created treatment plans, which are stored with their associated anatomical information in studies, to find the one with the most similar anatomy to a new patient. The analyses would be performed in parallel on the cloud to decrease the computation time of finding this plan. METHODS: The system used SlicerRT, a radiation therapy toolkit for the open-source platform 3D Slicer, for its tools to perform the similarity analysis algorithm. Amazon Web Services was used for the cloud instances on which the analyses were performed, as well as for storage of the radiation therapy studies and messaging between the instances and a master local computer. A module was built in SlicerRT to provide the user with an interface to direct the system on the cloud, as well as to perform other related tasks. RESULTS: The cloud-based system out-performed previous methods of conducting the similarity analyses in terms of time, as it analyzed 100 studies in approximately 13 minutes, and produced the same similarity values as those methods. It also scaled up to larger numbers of studies to analyze in the database with a small increase in computation time of just over 2 minutes. CONCLUSION: This system successfully analyzes a large database of radiation therapy studies and finds the one that is most similar to a new patient, which represents a potential step forward in achieving feasible adaptive radiation therapy replanning.

Evaluation of semi-passive treatment technologies for septic lagoon capacity expansion

In Canada, increases in rural development has led to a growing need to effectively manage the resulting municipal and city sewage without the addition of significant cost- and energy- expending infrastructure. Storring Septic Service Limited is a family-owned, licensed wastewater treatment facility located in eastern Ontario. It makes use of a passive waste stabilization pond system to treat and dispose of waste and wastewater in an environmentally responsible manner. Storring Septic, like many other similar small-scale wastewater treatment facilities across Canada, has the potential to act as a sustainable eco-engineered facility that municipalities and service providers could utilize to manage and dispose of their wastewater. However, it is of concern that the substantial inclusion of third party material could be detrimental to the stability and robustness of the pond system. In order to augment the capacity of the current facility, and ensure it remains a self-sustaining system with the capacity to safely accept septage from other sewage haulers, it was hypothesized that pond effluent treatment could be further enhanced through the incorporation of one of three different technology solutions, which would allow the reduction of wastewater quality parameters below existing regulatory effluent discharge limits put in place by Ontario’s Ministry of the Environment and Climate Change (MOECC). Two of these solutions make use of biofilm technologies in order to enhance the removal of wastewater parameters of interest, and the third utilizes the natural water filtration capabilities of zebra mussels. Pilot-scale testing investigated the effects of each of these technologies on treatment performance under both cold and warm weather operation. This research aimed to understand the important mechanisms behind biological filtration methods in order to choose and optimize the best treatment strategy for full-scale testing and implementation. In doing so, a recommendation matrix was elaborated provided with the potential to be used as a universal operational strategy for wastewater treatment facilities located in environments of similar climate and ecology.

An Exploration of the challenges associated with software logging in large systems

Over the past few years, logging has evolved from from simple printf statements to more complex and widely used logging libraries. Today logging information is used to support various development activities such as fixing bugs, analyzing the results of load tests, monitoring performance and transferring knowledge. Recent research has examined how to improve logging practices by informing developers what to log and where to log. Furthermore, the strong dependence on logging has led to the development of logging libraries that have reduced the intricacies of logging, which has resulted in an abundance of log information. Two recent challenges have emerged as modern software systems start to treat logging as a core aspect of their software. In particular, 1) infrastructural challenges have emerged due to the plethora of logging libraries available today and 2) processing challenges have emerged due to the large number of log processing tools that ingest logs and produce useful information from them. In this thesis, we explore these two challenges. We first explore the infrastructural challenges that arise due to the plethora of logging libraries available today. As systems evolve, their logging infrastructure has to evolve (commonly this is done by migrating to new logging libraries). We explore logging library migrations within Apache Software Foundation (ASF) projects. We i find that close to 14% of the pro jects within the ASF migrate their logging libraries at least once. For processing challenges, we explore the different factors which can affect the likelihood of a logging statement changing in the future in four open source systems namely ActiveMQ, Camel, Cloudstack and Liferay. Such changes are likely to negatively impact the log processing tools that must be updated to accommodate such changes. We find that 20%-45% of the logging statements within the four systems are changed at least once. We construct random forest classifiers and Cox models to determine the likelihood of both just-introduced and long-lived logging statements changing in the future. We find that file ownership, developer experience, log density and SLOC are important factors in determining the stability of logging statements.