Range and dose verification in proton therapy using proton-induced positron emitters and recurrent neural networks (RNNs)
Range and dose verification in proton therapy using proton-induced positron emitters and recurrent neural networks (RNNs)
复制标题
使用质子诱导正电子发射器和循环神经网络 (RNN) 验证质子治疗的范围和剂量
DOI：
10.1088/1361-6560/ab3564
复制
发表时间：
2019
影响因子：
3.5
通讯作者：
Hao Peng
中科院分区：
文献类型：
--
作者：
Chuang Liu;Zhongxing Li;Wenbin Hu;L. Xing;Hao Peng
基于质子感生正电子发射体测量的在线质子射程/剂量验证是质子治疗质量保证的一种有前途的策略。由于正电子发射体的剂量分布和活性分布之间存在非线性相关性，我们的目标是使用递归神经网络模型（LSTM，BiLSTM，GRU，BiGRU和Seq 2seq）建立它们之间的关系。使用Geant 4 -10.3工具包和基于CT的患者体模，使用点扫描质子系统进行模拟。得到了正电子发射体和辐射剂量的一维分布。训练数据针对不同的射束能量、照射位置和计数统计进行建模。定量研究了射程和剂量的预测精度。研究了包括解剖信息（CT图像中的HU值）对预测性能的影响。BiGRU表现出最稳定和准确的性能，具有良好的泛化能力，特别是包含解剖信息。当1D放射性分布的信噪比（SNR）约为3时，范围精度可以在0.5 mm以内，接近峰值区域的剂量精度<5%（所有数据集的预测和原始输入之间的相对不确定性）。验证了基于RNN框架的质子射程和剂量验证的可行性。基于RNN的框架有望为在线监测、质量保证提供可靠有效的方法，并最终实现自适应质子治疗。
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, we aim to establish their relationship using recurrent neural network models (LSTM, BiLSTM, GRU, BiGRU and Seq2seq). Simulations were carried out with a spot-scanning proton system using Geant4-10.3 toolkit and a CT-based patient phantom. The 1D distributions of positron emitters and radiation dose were obtained. Training data were modeled for different beam energy, irradiation positions and counting statistics. The prediction accuracy of range and dose were quantitatively studied. The impact of including anatomical information (HU values in CT images) on the prediction performance was investigated. The BiGRU demonstrates the most stable and accurate performance with good capability of generalization, especially with the inclusion of anatomical information. When the signal-to-noise ratio (SNR) of the 1D activity profiles is about 3, the range accuracy can be within 0.5 mm and the dose accuracy close to the peak region is <5% (relative uncertainty between prediction and raw input for all datasets). The feasibility of proton range and dose verification using the RNN-based framework was demonstrated. The RNN-based framework promises to provide a reliable and effective way for online monitoring, quality assurance and ultimately allows for adaptive proton therapy.
影响因子：
3.8
作者：
Parodi K;Polf JC
通讯作者：
Polf JC