Technical Note: Machine learning approaches for range and dose verification in proton therapy using proton‐induced positron emitters

作者：Zhongxing Li, Yiang Wang, Yajun Yu, Kuanjun Fan, Lei Xing, Hao Peng · 发表于：Medical Physics · 年份：2019 · DOI：10.1002/mp.13827 · 被引用次数：23 · 研究领域：Radiation Therapy and Dosimetry、Advanced Radiotherapy Techniques、Radiation Detection and Scintillator Technologies

PURPOSE/OBJECTIVE(S): 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 presence of noise, machine learning approaches were proposed to establish their relationship. MATERIALS/METHODS: Simulations were carried out with a spot-scanning proton system using GATE-8.0 and Geant4-10.3 toolkit with a computed tomography (CT)-based patient phantom. The one-dimensional (1D) distributions of positron emitters and radiation dose were obtained. A feedforward neural network classification model comprising two hidden layers, was developed to estimate whether the range is within a preset threshold. A recurrent neural network (RNN) regression model comprising three layers and ten neurons in each hidden layer was developed to estimate dose distribution. The performance was quantitatively studied in terms of mean squared error (MSE) and mean absolute error (MAE) under different signal-to-noise ratio (SNR) values. 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 irradiat...