We present DeepDose, a deep learning framework for fast dose calculations in radiation therapy. Given a patient anatomy and linear-accelerator IMRT multi-leaf-collimator shape or segment, a novel set of physics-based inputs is calculated that encode the linac machine parameters into the underlying anatomy. These inputs are then used to train a deep convolutional network to derive the dose distribution of individual MLC shapes on a given patient anatomy. In this work we demonstrate the proof-of-concept application of DeepDose on 101 prostate patients treated in our clinic with fixed-beam IMRT. The ground-truth data used for training, validation and testing of the prediction were calculated with a state-of-the-art MonteCarlo dose engine at 1% statistical uncertainty per segment. A deep convolution network was trained using the data of 80 patients at the clinically used 3 mm3grid spacing while 10 patients were used for validation. For another 11 independent test patients, the network was able to accurately estimate the segment doses from the clinical plans of each patient passing the clinical QA when compared with the MonteCarlo calculations, yielding on average 99.9%±0.3% for the forward calculated patient plans at 3%/3mm gamma tests. Dose prediction using the trained network was very fast at approximately 0.9 seconds for the input generation and 0.6 seconds for single GPU inference per segment and 1 minute per patient in total. The overall performance of this dose calculation framework in terms of both accuracy and inference speed, makes it compelling for online adaptive workflows where fast segment dose calculations are needed.

中文翻译：

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DeepDose：面向使用深度学习进行放射疗法的快速剂量计算引擎。

我们介绍了DeepDose，这是用于放射治疗中快速剂量计算的深度学习框架。给定患者解剖结构和直线加速器IMRT多叶准直器的形状或片段，将计算出一组新的基于物理的输入，这些输入将直线加速器参数编码为基础解剖结构。这些输入然后用于训练深度卷积网络，以得出给定患者解剖结构上各个MLC形状的剂量分布。在这项工作中，我们演示了DeepDose在101名接受固定束IMRT治疗的前列腺癌患者中的概念验证应用。使用最先进的MonteCarlo剂量引擎以每段1％的统计不确定性计算用于预测的训练，验证和测试的真实数据。在临床使用的3 mm3网格间距下，使用80名患者的数据训练了深度卷积网络，同时使用10名患者进行验证。对于另外11名独立测试患者，与MonteCarlo计算结果相比，该网络能够从每位通过临床QA的患者的临床计划中准确估算出分段剂量，对于前瞻性计算的患者计划，平均收益为99.9％±0.3％ 3％/ 3mm伽玛测试。使用训练有素的网络进行剂量预测的速度非常快，输入生成大约为0.9秒，每段单个GPU推理大约为0.6秒，每位患者总共1分钟。就准确度和推断速度而言，此剂量计算框架的整体性能，