摘要目的:评估采用单一MRI模拟定位实现鼻咽癌光子和质子放疗计划剂量计算的可行性。方法:回顾性分析2020年1月至2021年12月在中国医学科学院肿瘤医院接受放射治疗的100例鼻咽癌患者的T 1加权MRI图像和CT图像。利用深度学习网络模型将MRI转换成伪CT，训练集、验证集和测试集分别包括70例、10例和20例。深度学习方法采用监督学习的卷积神经网络（CNN）和无监督学习的循环一致性生成对抗网络（CycleGAN）两种方法。利用平均绝对误差（MAE）和结构相似性（SSIM）等定量评估图像质量，利用γ通过率和剂量体积直方图（DVH）评估剂量。用Wilcoxon符号秩检验来统计分析伪CT的图像质量。 结果:图像生成方面，CNN和CycleGAN模型的MAE分别为（91.99±19.98）HU和（108.30±20.54）HU，SSIM分别为0.97±0.01和0.96±0.01。剂量学方面，伪CT用于光子剂量计算的准确性高于质子。光子放疗计划的γ通过率（3 mm/3%）分别为：CNN模型99.90%±0.13%，CycleGAN模型99.87%±0.14%，且均大于98%。质子放疗计划分别为：CNN模型98.65%±0.64%，CycleGAN模型97.69%±0.86%。DVH的指标显示，伪CT的光子计划中剂量数值一致性优于质子计划。结论:基于深度学习的模型能从MRI图像生成准确的伪CT，大多数剂量学差异都在光子和质子放疗的临床可接受范围内，只用MRI成像的工作流程对鼻咽癌患者放疗是可行的。但与原始CT相比，伪CT图像在鼻腔区域的CT值误差较大，临床使用时应特别注意。

abstractsObjective:To evaluate the feasibility of using MRI-only simulation images for dose calculation of both photon and proton radiotherapy for nasopharyngeal carcinoma cases.Methods:T 1-weighted MRI images and CT images of 100 patients with nasopharyngeal carcinoma treated with radiotherapy in Cancer Hospital of Chinese Academy of Medical Sciences from January 2020 to December 2021 were retrospectively analyzed. MRI images were converted to generate pseudo-CT images by using deep learning network models. The training set, validation set and test set included 70 cases, 10 cases and 20 cases, respectively. Convolutional neural network (CNN) and cycle-consistent generative adversarial neural network (CycleGAN) were exploited. Quantitative assessment of image quality was conducted by using mean absolute error (MAE) and structural similarity (SSIM), etc. Dose assessment was performed by using 3D-gamma pass rate and dose-volume histogram (DVH). The quality of pseudo-CT images generated was statistically analyzed by Wilcoxon signed-rank test. Results:The MAE of the CNN and CycleGAN was (91.99±19.98) HU and (108.30±20.54) HU, and the SSIM was 0.97±0.01 and 0.96±0.01, respectively. In terms of dosimetry, the accuracy of pseudo-CT for photon dose calculation was higher than that of the proton plan. For CNN, the gamma pass rate (3 mm/3%) of the photon radiotherapy plan was 99.90%±0.13%. For CycleGAN, the value was 99.87%±0.34%. The gamma pass rates of proton radiotherapy plans were 98.65%±0.64% (CNN, 3 mm/3%) and 97.69%±0.86% (CycleGAN, 3 mm/3%). For DVH, the dose calculation accuracy in the photon plan of pseudo-CT was better than that of the proton plan.Conclusions:The deep learning-based model generated accurate pseudo-CT images from MR images. Most dosimetric differences were within clinically acceptable criteria for photon and proton radiotherapy, demonstrating the feasibility of an MRI-only workflow for radiotherapy of nasopharyngeal cancer. However, compared with the raw CT images, the error of the CT value in the nasal cavity of the pseudo-CT images was relatively large and special attention should be paid during clinical application.