Ma Xiangyu, Chen Xinyuan, Li Jingwen, Wang Yu, Men Kuo, Dai Jianrong
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Cloud Computing and Big Date Research Institute, China Academy of Information and Communications Technology, Beijing, China.
Front Oncol. 2021 Sep 8;11:713617. doi: 10.3389/fonc.2021.713617. eCollection 2021.
Radical radiotherapy is the main treatment modality for early and locally advanced nasopharyngeal carcinoma (NPC). Magnetic resonance imaging (MRI) has the advantages of no ionizing radiation and high soft-tissue resolution compared to computed tomography (CT), but it does not provide electron density (ED) information for radiotherapy planning. Therefore, in this study, we developed a pseudo-CT (pCT) generation method to provide necessary ED information for MRI-only planning in NPC radiotherapy.
Twenty patients with early-stage NPC who received radiotherapy in our hospital were investigated. First, 1433 sets of paired T1 weighted magnetic resonance (MR) simulation images and CT simulation images were rigidly registered and preprocessed. A 16-layer U-Net was used to train the pCT generative model and a "pix2pix" generative adversarial network (GAN) was also trained to compare with the pure U-Net regrading pCT quality. Second, the contours of all target volumes and organs at risk in the original CT were transferred to the pCT for planning, and the beams were copied back to the original CT for reference dose calculation. Finally, the dose distribution calculated on the pCT was compared with the reference dose distribution through gamma analysis and dose-volume indices.
The average time for pCT generation for each patient was 7.90 ± 0.47 seconds. The average mean (absolute) error was -9.3 ± 16.9 HU (102.6 ± 11.4 HU), and the mean-root-square error was 209.8 ± 22.6 HU. There was no significant difference between the pCT quality of pix2pix GAN and that of pure U-Net (p > 0.05). The dose distribution on the pCT was highly consistent with that on the original CT. The mean gamma pass rate (2 mm/3%, 10% low dose threshold) was 99.1% ± 0.3%, and the mean absolute difference of nasopharyngeal PGTV D and PTV V were 0.4% ± 0.2% and 0.1% ± 0.1%.
The proposed deep learning model can accurately predict CT from MRI, and the generated pCT can be employed in precise dose calculations. It is of great significance to realize MRI-only planning in NPC radiotherapy, which can improve structure delineation and considerably reduce additional imaging dose, especially when an MR-guided linear accelerator is adopted for treatment.
根治性放射治疗是早期和局部晚期鼻咽癌（NPC）的主要治疗方式。与计算机断层扫描（CT）相比，磁共振成像（MRI）具有无电离辐射和软组织分辨率高的优点，但它不能为放射治疗计划提供电子密度（ED）信息。因此，在本研究中，我们开发了一种伪CT（pCT）生成方法，为NPC放射治疗中仅基于MRI的计划提供必要的ED信息。
对我院20例接受放射治疗的早期NPC患者进行研究。首先，对1433组配对的T1加权磁共振（MR）模拟图像和CT模拟图像进行刚性配准和预处理。使用16层U-Net训练pCT生成模型，并训练一个“pix2pix”生成对抗网络（GAN），以与纯U-Net在pCT质量方面进行比较。其次，将原始CT中所有靶区体积和危及器官的轮廓转移到pCT上进行计划，并将射野复制回原始CT进行参考剂量计算。最后，通过伽马分析和剂量体积指数将在pCT上计算的剂量分布与参考剂量分布进行比较。
每位患者生成pCT的平均时间为7.90±0.47秒。平均平均（绝对）误差为-9.3±16.9 HU（102.6±11.4 HU），均方根误差为209.8±22.6 HU。pix2pix GAN的pCT质量与纯U-Net的pCT质量之间无显著差异（p>0.05）。pCT上的剂量分布与原始CT上的剂量分布高度一致。平均伽马通过率（2 mm/3%，10%低剂量阈值）为99.1%±0.3%，鼻咽PGTV D和PTV V的平均绝对差异分别为0.4%±0.2%和0.1%±0.1%。
所提出的深度学习模型可以从MRI准确预测CT，生成的pCT可用于精确剂量计算。在NPC放射治疗中实现仅基于MRI的计划具有重要意义，这可以改善靶区勾画并显著减少额外的成像剂量，尤其是在采用MR引导直线加速器进行治疗时。
