LAUSR

Purpose It is standard practice to perform QA of IMRT and VMAT plans by delivering the plans to the EPID (Electronic Portal Imaging Device) and comparing the measured dose to the calculated dose by an independent dose calculation engine prior to treatment. However, this procedure is both time consuming and reduces the patient throughput because the whole treatment must be delivered and measured using a clinical accelerator. In addition, noise and drift of the EPID require considerable extra resources to investigate false dose deviations and to perform repetitive EPID measurements. Accordingly, there is a need for a more streamlined QA approach with the same level of patient safety. Methods The Treatment Delivery Tool is an in house-developed C# application that can analyze both Varian Clinac and Varian TrueBeam treatment delivery log files. For each treatment the deviation between the delivered and planned fluence is calculated using the planned/actual MLC positions and the planned/actual MU for each time step in the log file. In addition, the application calculates all key technical parameters of the treatment delivery (e.g. max. MLC deviation, max. MU deviation, and max. MLC speed). Results The Treatment Delivery Tool automatically analyzes all daily treatments of an accelerator within one minute using a standard PC. Over a test period of two months 14 treatments with a significant deviation of the delivered fluence (more than 2% for more than 2% of the fluence area) were found. The deviations are related to a high MLC modulation (small MLC gap) and a large MU deviation. The test period also demonstrates that the Treatment Delivery Tool detects treatment errors with a much higher accuracy than possible with EPID-based QA. Conclusions The Treatment Delivery Tool is an effective and accurate tool for detecting dose delivery errors. In our department the application is complemented by an independent dose calculation by MobiusCalc (Mobius, USA) and a comprehensive accelerator QA program detecting any significant deviation between treatment delivery and data in the log file. This combined approach is very efficient and provides the same level of patient safety as for EPID-based patient-specific QA.