SU-D-105-04: Automated QA Analysis of VMAT Delivery Errors Using Trajectory Log Files

Abstract

Purpose: To investigate the sensitivity of trajectory log analysis for detecting small VMAT delivery errors with an automated program. Methods: For each treatment, Varian TrueBeam™ generates a set of trajectory log files that record accumulated MU, MLC positions, and gantry angle every 10 ms during delivery. An automated computer program analyzes discrepancies between planned and actual leaf positions. Any leaf that exceeds 0.5 mm discrepancy for >15% of the total beam‐on time is flagged and an alert sent to a physicist for further investigation. To validate the method, 6 different “induced‐error” VMAT plans were generated by modifying a lung VMAT plan with an intended error: gantry angle error of 0.5 and 1.0 deg, position error of one leaf of 0.5, 1.0, 1.5, and 2.0 mm. All plans were delivered and trajectory logs were collected. The dosimetric effect of the induced errors was evaluated by reconstructing dose distributions from the trajectory logs and comparing to the original error‐free plan. Results: The proposed method is sensitive and can detect gantry error down to 0.5 deg and MLC leaf error of 0.5 mm without any false positives (i.e. no error detected for other leaves). No dose difference was observed for gantry angle errors of 0.5–1.0 deg, while the maximum dose discrepancy from a MLC position error of 0.5 mm was 1% and increased to 3% for a leaf error of 2 mm, with a passing rate of 99.6% and 97.7% for gamma analysis (1%/0 mm), respectively. Conclusion: Analysis of trajectory log files is highly sensitive and provides an efficient and accurate method of detecting treatment delivery errors down to sub‐millimeters. The automated program can be run daily for treatment validation and can be used also as a pre‐treatment QA tool. Research grant from Varian Medical Systems.

Use of a constrained hierarchical optimization dataset enhances knowledge‐based planning as a quality assurance tool for prostate bed irradiation

“…KBP will be used as part of our suite of automated QA tools, flagging plans that are not consistent with the model for further review by the clinical staff. Other QA tools in the suite include an automated plan checker and automated treatment delivery verification . KBP models built with CHO plans could also be employed as part of a decision support framework to furnish clinicians with estimated dose distributions immediately after segmentation.…”

“…Other QA tools in the suite include an automated plan checker 24 and automated treatment delivery verification. 25 KBP models built with CHO plans could also be employed as part of a decision support framework to furnish clinicians with estimated dose distributions immediately after segmentation.…”