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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Purpose: To optimize clinical efficiency and shorten patient wait time by minimizing the time and effort required to perform the Winston‐Lutz test before stereotactic radiosurgery (SRS) through automation of the delivery, analysis, and documentation of results. Methods: The radiation fields of the Winston‐Lutz (WL) test were created in a “machine‐QA patient” saved in ARIA for use before SRS cases. Images of the BRW target ball placed at mechanical isocenter are captured with the portal imager for each of four, 2cm×2cm, MLC‐shaped beams. When the WL plan is delivered and closed, this event is detected by in‐house software called EventNet which automates subsequent processes with the aid of the ARIA web services. Images are automatically retrieved from the ARIA database and analyzed to determine the offset of the target ball from radiation isocenter. The results are posted to a website and a composite summary image of the results is pushed back into ImageBrowser for review and authenticated documentation. Results: The total time to perform the test was reduced from 20‐25 minutes to less than 4 minutes. The results were found to be more accurate and consistent than the previous method which used radiochromic film. The images were also analyzed with DoseLab for comparison. The difference between the film and automated WL results in the X and Y direction and the radius were (−0.17 +/− 0.28) mm, (0.21 +/− 0.20) mm and (−0.14 +/− 0.27) mm, respectively. The difference between the DoseLab and automated WL results were (−0.05 +/− 0.06) mm, (−0.01 +/− 0.02) mm and (0.01 +/− 0.07) mm, respectively. Conclusions: This process reduced patient wait times by 15–20 minutes making the treatment machine available to treat another patient. Accuracy and consistency of results were improved over the previous method and were comparable to other commercial solutions. Access to the ARIA web services is made possible through an Eclipse co‐development agreement with Varian Medical Systems.

Purpose: Quality assurance is an essential task in radiotherapy that often requires many manual tasks. We investigate the use of an event driven framework in conjunction with software agents to automate QA and eliminate wait times. Methods: An in house developed subscription‐publication service, EventNet, was added to the Aria OIS to be a message broker for critical events occurring in the OIS and software agents. Software agents operate without user intervention and perform critical QA steps. The results of the QA are documented and the resulting event is generated and passed back to EventNet. Users can subscribe to those events and receive messages based on custom filters designed to send passing or failing results to physicists or dosimetrists. Agents were developed to expedite the following QA tasks: Plan Revision, Plan 2nd Check, SRS Winston‐Lutz isocenter, Treatment History Audit, Treatment Machine Configuration. Results: Plan approval in the Aria OIS was used as the event trigger for plan revision QA and Plan 2nd check agents. The agents pulled the plan data, executed the prescribed QA, stored the results and updated EventNet for publication. The Winston Lutz agent reduced QA time from 20 minutes to 4 minutes and provided a more accurate quantitative estimate of radiation isocenter. The Treatment Machine Configuration agent automatically reports any changes to the Treatment machine or HDR unit configuration. The agents are reliable, act immediately, and execute each task identically every time. Conclusion: An event driven framework has inverted the data chase in our radiotherapy QA process. Rather than have dosimetrists and physicists push data to QA software and pull results back into the OIS, the software agents perform these steps immediately upon receiving the sentinel events from EventNet. Mr Keranen is an employee of Varian Medical Systems. Dr. Moran's institution receives research support for her effort for a linear accelerator QA project from Varian Medical Systems. Other quality projects involving her effort are funded by Blue Cross Blue Shield of Michigan, Breast Cancer Research Foundation, and the NIH.