Abstract
With emerging techniques for tracking and gating methods in radiotherapy of lung cancer patients, there is an increasing need for efficient four-dimensional Monte Carlo (4DMC) based quality assurance (QA). An automated and flexible workflow for 4DMC QA, based on the 4DdefDOSXYZnrc user code, has been developed in python. The workflow has been tested and verified using an in-house developed dosimetry system comprised of a dynamic thorax phantom constructed for plastic scintillator dosimetry. The workflow is directly compatible with any treatment planning system and can also be triggered by the appearance of linac log files. It has minimum user interaction and, with the use of linac log files, it provides a method for verification of the actually delivered dose in the patient geometry.
Highlights
Motion management in external beam radiotherapy is becoming increasingly sophisticated and the demands on quality assurance (QA) of advanced radiotherapy are increasing
Workflow for four-dimensional Monte Carlo In order to incorporate the synchronization between the dynamic beam configuration and the motion of the patient anatomy, MC simulations were carried out using 4DdefDOSXYZnrc
The hypothesis was that the deviations between measured and treatment planning systems (TPS) calculated doses were true deviations as a result of the tumor motion and the difficulties for the TPS to accurately account for the lack of charged particle equilibrium (CPE)
Summary
Motion management in external beam radiotherapy is becoming increasingly sophisticated and the demands on quality assurance (QA) of advanced radiotherapy are increasing. Many commercial treatment planning systems (TPS) have recognized difficulties to accurately calculate dose for dynamic treatments due to challenges related to breathing motion and heterogeneities. This has for example been shown for deep-inspiration breathhold (DIBH) intensity-modulated radiotherapy (IMRT) of lung cancer patients, where Monte Carlo (MC) calculations revealed large inaccuracies in the dose calculated by the TPS [1]. An automated MCQA workflow with minimum user interaction is much more desirable This MCQA workflow would enable four-dimensional Monte Carlo (4DMC) which models synchronously the dynamic beam configurations and the motion and deformation of the patient anatomy
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