Department of Radiation Oncology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA 94305-5847, United States of America.
Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, People's Republic of China; Department of Oncology, Shanghai Medical College Fudan University, Shanghai 200032, People's Republic of China.
Phys Med Biol. 2020 Dec 22;65(24):245035. doi: 10.1088/1361-6560/abca05.
Accurate and efficient dose calculation is an important prerequisite to ensure the success of radiation therapy. However, all the dose calculation algorithms commonly used in current clinical practice have to compromise between calculation accuracy and efficiency, which may result in unsatisfactory dose accuracy or highly intensive computation time in many clinical situations. The purpose of this work is to develop a novel dose calculation algorithm based on the deep learning method for radiation therapy. In this study we performed a feasibility investigation on implementing a fast and accurate dose calculation based on a deep learning technique. A two-dimensional (2D) fluence map was first converted into a three-dimensional (3D) volume using ray traversal algorithm. 3D U-Net like deep residual network was then established to learn a mapping between this converted 3D volume, CT and 3D dose distribution. Therefore an indirect relationship was built between a fluence map and its corresponding 3D dose distribution without using significantly complex neural networks. Two hundred patients, including nasopharyngeal, lung, rectum and breast cancer cases, were collected and applied to train the proposed network. Additional 47 patients were randomly selected to evaluate the accuracy of the proposed method through comparing dose distributions, dose volume histograms and clinical indices with the results from a treatment planning system (TPS), which was used as the ground truth in this study. The proposed deep learning based dose calculation algorithm achieved good predictive performance. For 47 tested patients, the average per-voxel bias of the deep learning calculated value and standard deviation (normalized to the prescription), relative to the TPS calculation, is 0.17%±2.28%. The average deep learning calculated values and standard deviations for relevant clinical indices were compared with the TPS calculated results and the t-test p-values demonstrated the consistency between them. In this study we developed a new deep learning based dose calculation method. This approach was evaluated by the clinical cases with different sites. Our results demonstrated its feasibility and reliability and indicated its great potential to improve the efficiency and accuracy of radiation dose calculation for different treatment modalities.
准确高效的剂量计算是保证放射治疗成功的重要前提。然而，目前临床实践中使用的所有剂量计算算法都必须在计算精度和效率之间进行权衡，这可能导致在许多临床情况下剂量精度不理想或计算时间过长。本工作旨在开发一种基于深度学习方法的放射治疗新剂量计算算法。在这项研究中，我们对基于深度学习技术实现快速准确的剂量计算进行了可行性研究。首先，通过射线追踪算法将二维（2D）通量图转换为三维（3D）体积。然后，建立了类似于 3D U-Net 的深度残差网络，以学习该转换后的 3D 体积、CT 和 3D 剂量分布之间的映射关系。因此，在不使用复杂神经网络的情况下，建立了通量图与其对应的 3D 剂量分布之间的间接关系。收集了 200 例患者，包括鼻咽癌、肺癌、直肠癌和乳腺癌病例，用于训练所提出的网络。另外随机选择了 47 例患者，通过比较剂量分布、剂量体积直方图和临床指标与作为本研究基准的治疗计划系统（TPS）的结果，来评估所提出方法的准确性。所提出的基于深度学习的剂量计算算法具有良好的预测性能。对于 47 例测试患者，与 TPS 计算相比，深度学习计算值的平均每个体素偏差（归一化至处方剂量）及其标准差为 0.17%±2.28%。将相关临床指标的深度学习计算值及其标准差与 TPS 计算结果进行比较，t 检验 p 值表明它们之间的一致性。在这项研究中，我们开发了一种新的基于深度学习的剂量计算方法。通过不同部位的临床病例对该方法进行了评估。我们的结果证明了其可行性和可靠性，并表明它有很大的潜力提高不同治疗方式的放射剂量计算的效率和准确性。
