Proper quality assurance (QA) of the radiotherapy process can be time-consuming and expensive. Many QA efforts, such as data export and import, are inefficient when done by humans. Additionally, humans can be unreliable, lose attention, and fail to complete critical steps that are required for smooth operations. In our group we have sought to break down the QA tasks into separate steps and to automate those steps that are better done by software running autonomously or at the instigation of a human. A team of medical physicists and software engineers worked together to identify opportunities to streamline and automate QA. Development efforts follow a formal cycle of writing software requirements, developing software, testing and commissioning. The clinical release process is separated into clinical evaluation testing, training, and finally clinical release. We have improved six processes related to QA and safety. Steps that were previously performed by humans have been automated or streamlined to increase first-time quality, reduce time spent by humans doing low-level tasks, and expedite QA tests. Much of the gains were had by automating data transfer, implementing computer-based checking and automation of systems with an event-driven framework. These coordinated efforts by software engineers and clinical physicists have resulted in speed improvements in expediting patient-sensitive QA tests.
identified one outlier cluster (0.34%) along Leaf offset Constancy (LoC) axis that coincided with TG-142 limits. Conclusion: Machine learning methods based on SVDD clustering are promising for developing automated QA tools and providing insights into their reliability and reproducibility.
Large scientific collaborations as well as universities have a growing need for multimedia archiving of meetings and courses. Collaborations need to disseminate training and news to their wide-ranging members, and universities seek to provide their students with more useful studying tools. The University of Michigan ATLAS Collaboratory Project has been involved in the recording and archiving of multimedia lectures since 1999. Our software and hardware architecture has been used to record events for CERN, ATLAS, many units inside the University of Michigan, Fermilab, the American Physical Society and the International Conference on Systems Biology at Harvard. Until 2006 our group functioned primarily as a tiny research/development team with special commitments to the archiving of certain ATLAS events. In 2006 we formed the MScribe project, using a larger scale, and highly automated recording system to record and archive eight University courses in a wide array of subjects. Several robotic carts are wheeled around campus by unskilled student helpers to automatically capture and post to the Web audio, video, slides and chalkboard images. The advances the MScribe project has made in automation of these processes, including a robotic camera operator and automated video processing, are now being used to record ATLAS Collaboration events, making them available more quickly than before and enabling the recording of more events.
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