(Monday, 7/15/2019) 3:45 PM - 4:15 PM
Room: Exhibit Hall | Forum 6
Purpose: Online proton range/dose verification based on measurements of proton-induced positron emitters is a promising strategy for quality assurance in proton therapy. Because of the nonlinear correlation between the dose distribution and the activity distribution of positron emitters in addition to the adverse influence of noise, the relationship between these two needs to be established using machine learning approaches.
Methods: Simulations were carried out with a spot-scanning proton system using GATE-8.0 and Geant4-10.3 toolkit with a CT-based patient phantom. The 1D distributions of positron emitters and radiation dose were obtained. A feedforward neural network classification model comprising 2 hidden layers, was developed to estimate whether the range is within a preset threshold. A recursive neural network (RNN) regression model comprising 3 layers and 10 neurons in each hidden layer, was developed to estimate dose distribution.
Results: The feasibility of proton range and dose verification using the proposed neural network framework was demonstrated. The feedforward NN model achieves high classification accuracy close to 100% for individual classes without bias. The RNN model is able to accurately predict the 1D dose distribution for different energies and irradiation positions. When the signal-to-noise ratio (SNR) of the activity profiles is about 4.5 (after 8 proton pulses), the framework is able to predict the range within 1 mm uncertainty and the dose distribution within 10% uncertainty.
Conclusion: Our proposed technique may enable highly accurate online range and dose verification. The machine learning-based framework promises to provide a reliable and effective way for online monitoring, and ultimately allows for adaptive proton therapy.
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