The iterative design of radiotherapy treatment plans is time-consuming and labor-intensive. In order to provide a guidance to treatment planning, Asymmetric network (A-Net) is proposed to predict the optimal 3D dose distribution for lung cancer patients. A-Net was trained and tested in 392 lung cancer cases with the prescription doses of 50Gy and 60Gy. In A-Net, the encoder and decoder are asymmetric, able to preserve input information and to adapt the limitation of GPU memory. Squeeze and excitation (SE) units are used to improve the data-fitting ability. A loss function involving both the dose distribution and prescription dose as ground truth are designed. In the experiment, A-Net is separately trained and tested in the 50Gy and 60Gy dataset and most of the metrics A-Net achieve similar performance as HD-Unet and 3D-Unet, and some metrics slightly better. In the 50Gy-and-60Gy-combined dataset, most of the A-Net's metrics perform better than the other two. In conclusion, A-Net can accurately predict the IMRT dose distribution in the three datasets of 50Gy and 50Gy-and-60Gy-combined dataset.

中文翻译：

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非对称网络肺癌患者三维放疗最佳剂量分布预测

放射治疗方案的迭代设计费时费力。为了为治疗计划提供指导，提出了非对称网络（A-Net）来预测肺癌患者的最佳 3D 剂量分布。A-Net 在 392 例肺癌病例中接受了 50Gy 和 60Gy 处方剂量的训练和测试。在 A-Net 中，编码器和解码器是非对称的，能够保留输入信息并适应 GPU 内存的限制。挤压和激励 (SE) 单元用于提高数据拟合能力。设计了一个包含剂量分布和处方剂量作为基本事实的损失函数。在实验中，A-Net 在 50Gy 和 60Gy 数据集上分别进行了训练和测试，并且 A-Net 的大部分指标达到了与 HD-Unet 和 3D-Unet 相似的性能，和一些指标稍微好一点。在 50Gy 和 60Gy 组合的数据集中，A-Net 的大部分指标都比其他两个指标表现更好。总之，A-Net 可以准确预测 50Gy 和 50Gy 和 60Gy 组合数据集三个数据集中的 IMRT 剂量分布。