Abstract
Respiratory movement is one of the major challenges in modern lung cancer radiotherapy. Many attempts are made to account for this motion; including different motion encompassing, gating, and tracking methods. The latter is probably the most complex where real-time tumor localization is essential. Additionally, dose calculation algorithms have known issues with heterogeneities, and experimental dose verification is often based only on the accumulated dose. The time-dependent accuracy of treatment delivery is therefore rarely known, making it difficult to locate the underlying cause of a failed delivery. Organic plastic scintillator dosimetry was here used for verification of time-resolved Monte Carlo (MC) calculations. The MC user code 4DdefDOSXYZnrc/EGSnrc enables dose calculation in continuously moving anatomies by sampling a new geometry for each incident particle [1] . The MC code was modified to score the dose in pre-defined voxels for a given temporal resolution. The time-resolved MC dose was compared with scintillator measurements in an in-house developed dynamic thorax phantom containing a tumor (PMMA) embedded in a lung cylinder (Balsa wood) [2] . Measurements were conducted for one conventional and one RapidArc treatment plan with (a) the phantom static and tumor centered at the isocenter, (b) the phantom moving (sinusoidal; 25 Hz, 20 mm peak-to-peak amplitude) around the isocenter, and (c) the phantom moving according to (b) with a 2.5 cm longitudinal isocenter shift. Corresponding MC calculations were based on linac logfiles acquired from the measurement sessions, during which also the motion of the phantom was logged. Potential calculation and measurement deviations were separated from those due to motion and delivery of the dynamic treatment, verifying the accuracy of the time-resolved MC calculations. This study proposes a new method for time-resolved dose verification of advanced dynamic treatments and highlights the potential of both 4DMC simulations and the scintillator dosimetry for taking on this task.
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