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Comprehensive Automated Daily QA for Hypo- Fractionated Treatments

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C McGuinness

C McGuinness1*, O Morin2 , (1) ,,,(2) University of California San Francisco, San Francisco, CA


SU-E-T-88 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose: The trend towards more SBRT treatments with fewer high dose fractions places increased importance on daily QA. Patient plan specific QA with 3%/3mm gamma analysis and daily output constancy checks may not be enough to guarantee the level of accuracy required for SBRT treatments. But increasing the already extensive amount of QA procedures that are required is a daunting proposition. We performed a feasibility study for more comprehensive automated daily QA that could improve the diagnostic capabilities of QA without increasing workload.

Methods: We performed the study on a Siemens Artiste linear accelerator using the integrated flat panel EPID. We included square fields, a picket fence, overlap and representative IMRT fields to measure output, flatness, symmetry, beam center, and percent difference from the standard. We also imposed a set of machine errors: MLC leaf position, machine output, and beam steering to compare with the standard.

Results: Daily output was consistent within +/-1%. Change in steering current by 1.4% and 2.4% resulted in a 3.2% and 6.3% change in flatness. 1 and 2mm MLC leaf offset errors were visibly obvious in difference plots, but passed a 3%/3mm gamma analysis. A simple test of transmission in a picket fence can catch a leaf offset error of a single leaf by 1mm. The entire morning QA sequence is performed in less than 30 minutes and images are automatically analyzed.

Conclusion: Automated QA procedures could be used to provide more comprehensive information about the machine with less time and human involvement. We have also shown that other simple tests are better able to catch MLC leaf position errors than a 3%/3mm gamma analysis commonly used for IMRT and modulated arc treatments. Finally, this information could be used to watch trends of the machine and predict problems before they lead to costly machine downtime.

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