Adding Run-Time Monitoring to UML-RT by Modifying the Papyrus-RT Code Generator

In Model-Driven Engineering (MDE), the developer creates a model using a language such as Unified Modeling Language (UML) or UML for Real-Time (UML-RT) and uses tools such as Papyrus or Papyrus-RT that generate code for them based on the model they create. Tracing allows developers to get insights such as which events occur and timing information into their own application as it runs. We try to add monitoring capabilities using Linux Trace Toolkit: next generation (LTTng) to models created in UML-RT using Papyrus-RT. The implementation requires changing the code generator to add tracing statements for the events that the user wants to monitor to the generated code. We also change the makefile to automate the build process and we create an Extensible Markup Language (XML) file that allows developers to view their traces visually using Trace Compass, an Eclipse-based trace viewing tool. Finally, we validate our results using three models we create and trace.

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.