A Novel QA Process Based On Monte Carlo 2nd Check and Dose Reconstruction From Machine Log File On TomoTherapy
Q Chen*, L Handsfield, D Wilson, J Larner, P Read, University of Virginia, Charlottesville, VASU-D-105-4 Sunday 2:05PM - 3:00PM Room: 105
Purpose: To develop a QA process that combines the pre-treatment plan verification with post-treatment delivery verification. Individual source of error such as dose calculator, MLC, and gantry angle can be isolated and analyzed.
Methods: A Monte-Carlo (MC) package, TomoPen, has been modified for pre-treatment plan verification. During TomoTherapy treatment, information such as ion chamber measured output, gantry and couch position, and MVCT exit-detectors fluence are recorded into a log file (rawdata). The log file is retrieved after each treatment and information about the treatment delivery is extracted. MLC movement is obtained through analysis of the exit-detector fluence. The actual delivery information is fed into MC dose calculation to evaluate the overall impact. 255 treatment deliveries in 38 patient plans were analyzed with this process.
Results: The average percent difference from the MC 2nd check on planned dose was -1.2%. MLC errors have strong correlation with plans. On average, MLC errors were 0.1+/-2.2%, which will produce negligible error. However, error as big as 1.7+/-0.8% was observed. It is also observed that the MLC errors did not have big variations day-to-day for the same patient plan. The LINAC output has been a big source of error. On average, the output dropped by 0.8% during each treatment, and varies day to day as target degrades. The output error for all treatment during a 7 month span has been 1.3+/-1.1%. MC reconstructed dose is found to correlated with the mean MLC and output errors. The log file analysis can be completed in less than 30 seconds and the MC calculation took 2-4 minutes.
Conclusion: We have developed a QA process capable of detecting errors from the planning to treatment delivery for TomoTherapy. The advantage over the existing phantom based QA method is that it provides more information about discrepancies in planning and delivery.
Funding Support, Disclosures, and Conflict of Interest: This study is supported in part by UVa George Amorino Pilot Grant.