Program Information
Detection of the Photon Target Damage in Varian Linac Based On Periodical Quality Assurance Measurements
S Gao*, P Balter , X Wang , R Sadagopan , J Pollard , UT MD Anderson Cancer Center, Houston, TX
Presentations
SU-E-T-245 (Sunday, July 12, 2015) 3:00 PM - 6:00 PM Room: Exhibit Hall
Purpose:To determine the best dosimetric metric measured by our routine QA devices for diagnosing photon target failure on a Varian C-series linac.
Methods:We have retrospectively reviewed and analyzed the dosimetry data from a Varian linac with a target degradation that was undiagnosed for one year. A failure in the daily QA symmetry tests was the first indication of an issue. The beam was steered back to a symmetric shape and water scans indicated the beam energy had changed but stayed within the manufacturer’s specifications and agreed reasonably with our treatment planning system data. After the problem was identified and the target was replaced, we retrospectively analyzed our QA data including diagonals normalized flatness (F_DN) from the daily device (DQA3), profiles from an ionization chamber array (IC Profiler), as well as profiles and PDDs from a 3D water Scanner (3DS). These metrics were cross-compared to determine which was the best early indicator of target degradation.
Results:A 3% change in FDN measured by the DQA3 was found to be an early indicator of target degradation. It is more sensitive than changes in output, symmetry, flatness or PDD. All beam shape metrics (flatness at dmax and 10 cm depth, and F_DN) indicated an energy increase while the PDD indicated an energy decrease. This disagreement between the beam-shape based energy metrics (F_DN and flatness) and PDD based energy metric may indicate target failure as opposed to an energy change resulting from changes in the incident electron energy.
Conclusion:Photon target degradation has been identified as a failure mode for linacs. The manufacturer’s test for this condition is highly invasive and requires machine down time. We have demonstrated that the condition could be caught early based upon data acquired during routine QA activities, such as the F_DN.
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