Patient‐specific quality assurance method for VMAT treatment delivery

Abstract

Volumetric modulated arc therapy (VMAT) is a system for intensity-modulated radiotherapy treatment delivery that achieves high dose conformality by optimizing the dose rate, gantry speed, and the leaf positions of the dynamic multileaf collimator (DMLC). The aim of this work is to present a practical approach for patient-specific volumetric reconstruction of the dose delivered of a VMAT treatment using the DMLC and treatment controller log (Dynalog) files. The accuracy of VMAT delivery was analyzed for five prostate patients. For each patient, a clinical treatment was delivered and values recorded in the log files for the gantry angle, dose rate, and leaf positions were converted to a new DICOM-compliant plan using a custom-developed software system. The plan was imported in a treatment planning system and the dose distribution was recreated on the original CT by simply recomputing the dose. Using the standard evaluation tools, it is straightforward to assess if reconstructed dose meets clinical endpoints, as well as to compare side-by-side reconstructed and original plans. The study showed that log files can be directly used for dose reconstruction without resorting to phantom measurements or setups. In all cases, analysis of the leaf positions showed a maximum error of -0.26 mm (mean of 0.15 mm). Gantry angle deviation was less than 1degree and the total MU was within 0.5 from the planned value. Differences between the reconstructed and the intended dose matrices were less than 1.46% for all cases. Measurements using the MATRIXX system in a phantom were used to validate the dosimetric accuracy of the proposed method, with an agreement of at least 96% in all pixels as measured using the gamma index. The methodology provides a volumetric evaluation of the dose reconstructed by VMAT plans which is easily achieved by automated analysis of Dynalog files without additional measurements or phantom setups. This process provides a valuable platform for adaptive therapy in the future.