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arXiv 2202.02653physics.med-ph
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基于毫秒级深度学习的蒙特卡罗精度质子剂量计算

Millisecond speed deep learning based proton dose calculation with Monte Carlo accuracy

Oscar Pastor-Serrano, Zoltán Perkó

摘要:

下一代在线和实时自适应放射治疗工作流程需要亚秒级的精确粒子输运模拟,这对于当前的分析笔形射束算法(PBA)或随机蒙特卡罗(MC)方法是不可行的。我们提出了一个数据驱动的毫秒速度剂量计算算法(DoTA)准确预测剂量沉积单能质子笔形束任意能量和病人的几何形状。考虑到质子的前向散射性质,我们将3D粒子输运框定为在束眼视图中建模一系列2D几何形状。DoTA结合了提取空间特征的卷积神经网络(例如,组织和密度对比)与Transformer自我关注主干,该主干在几何切片序列和表示射束能量的向量之间路由信息,并且被训练以使用80,000种不同的头部和颈部、肺和前列腺几何结构来预测质子子束的低噪声MC模拟。与地面真实MC计算相比,DoTA在5 ms内预测小射束剂量,具有99.37%(1%,3 mm)的非常高的伽马通过率,在精度和速度方面都显著提高了分析笔形射束算法。对于笔形射束,MC的准确性比PBA快100倍,我们的模型根据细射束的数量在10到15秒内计算出完整的治疗计划剂量,在9名测试患者中实现了99.70%(2%,2 mm)的伽马通过率。DoTA优于所有以前的分析笔形束和基于深度学习的方法,代表了数据驱动剂量计算的最新技术水平,甚至可以与商业GPU MC方法的速度直接竞争。提供自适应治疗所需的亚秒级速度,直接实施可以为放射治疗工作流程的其他步骤或其他模式(如氦或碳治疗)提供类似的好处。

英文摘要:

Next generation online and real-time adaptive radiotherapy workflows require precise particle transport simulations in sub-second times, which is unfeasible with current analytical pencil beam algorithms (PBA) or stochastic Monte Carlo (MC) methods. We present a data-driven millisecond speed dose calculation algorithm (DoTA) accurately predicting the dose deposited by mono-energetic proton pencil beams for arbitrary energies and patient geometries. Given the forward-scattering nature of protons, we frame 3D particle transport as modeling a sequence of 2D geometries in the beam's eye view. DoTA combines convolutional neural networks extracting spatial features (e.g., tissue and density contrasts) with a transformer self-attention backbone that routes information between the sequence of geometry slices and a vector representing the beam's energy, and is trained to predict low noise MC simulations of proton beamlets using 80,000 different head and neck, lung, and prostate geometries. Predicting beamlet doses in 5 ms with a very high gamma pass rate of 99.37% (1%, 3 mm) compared to the ground truth MC calculations, DoTA significantly improves upon analytical pencil beam algorithms both in precision and speed. Offering MC accuracy 100 times faster than PBAs for pencil beams, our model calculates full treatment plan doses in 10 to 15 s depending on the number of beamlets, achieving a 99.70% (2%, 2 mm) gamma pass rate across 9 test patients. Outperforming all previous analytical pencil beam and deep learning based approaches, DoTA represents a new state of the art in data-driven dose calculation and can directly compete with the speed of even commercial GPU MC approaches. Providing the sub-second speed required for adaptive treatments, straightforward implementations could offer similar benefits to other steps of the radiotherapy workflow or other modalities such as helium or carbon treatments.

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