论文标题
毫秒速度深度学习的质子剂量计算蒙特卡洛准确性
Millisecond speed deep learning based proton dose calculation with Monte Carlo accuracy
论文作者
论文摘要
下一代在线和实时自适应放射治疗工作流程需要在次秒内进行精确的颗粒传输模拟，这对于当前的分析铅笔梁算法（PBA）或随机蒙特卡洛（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），可以准确预测通过单能质子铅笔梁沉积的剂量，以实现任意能量和患者的几何形状。鉴于质子的正向散落性质，我们将3D粒子传输构建为对梁眼视图中2D几何的序列进行建模。 DOTA结合了卷积神经网络提取空间特征（例如，组织和密度对比）与变压器自我发作的主链，该主链在几何切片的顺序与代表光束能量的矢量之间进行途径，并经过训练，可以预测使用80,000个不同的头颈和颈部，lung和lung geometies和prostores geometies proton beamlets的低噪声MC模拟。与地面真相MC计算相比，5 ms的束剂量在5 ms的剂量中，非常高99.37％（1％，3毫米）的剂量，DOTA在精确和速度方面的分析铅笔梁算法时会显着改善。对于铅笔梁，提供MC准确性的100倍，我们的模型根据梁的数量计算了10至15 s的完整治疗计划剂量，在9个测试患者中达到了99.70％（2％，2 mm）Gamma Pass的剂量。 DOTA胜过所有以前的分析铅笔梁和基于深度学习的方法，代表了数据驱动的剂量计算中最新的最新状态，并且可以直接与甚至商业GPU MC方法的速度直接竞争。提供自适应治疗所需的次秒速度，直接实施可能会提供与放射疗法工作流程的其他步骤或其他方式（例如氦或碳治疗）相似的好处。
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.
