Xie Kai, Gao Liugang, Xi Qianyi, Zhang Heng, Zhang Sai, Zhang Fan, Sun Jiawei, Lin Tao, Sui Jianfeng, Ni Xinye
Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou 213000, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213000, China.
Center for Medical Physics, Nanjing Medical University, Changzhou 213003, China; Changzhou Key Laboratory of Medical Physics, Changzhou 213000, China.
Comput Methods Programs Biomed. 2023 Apr;231:107393. doi: 10.1016/j.cmpb.2023.107393. Epub 2023 Feb 1.
A generative adversarial network (TCBCTNet) was proposed to generate synthetic computed tomography (sCT) from truncated low-dose cone-beam computed tomography (CBCT) and planning CT (pCT). The sCT was applied to the dose calculation of radiotherapy for patients with breast cancer.
The low-dose CBCT and pCT images of 80 female thoracic patients were used for training. The CBCT, pCT, and replanning CT (rCT) images of 20 thoracic patients and 20 patients with breast cancer were used for testing. All patients were fixed in the same posture with a vacuum pad. The CBCT images were scanned under the Fast Chest M20 protocol with a 50% reduction in projection frames compared with the standard Chest M20 protocol. Rigid registration was performed between pCT and CBCT, and deformation registration was performed between rCT and CBCT. In the training stage of the TCBCTNet, truncated CBCT images obtained from complete CBCT images by simulation were used. The input of the CBCT→CT generator was truncated CBCT and pCT, and TCBCTNet was applied to patients with breast cancer after training. The accuracy of the sCT was evaluated by anatomy and dosimetry and compared with the generative adversarial network with UNet and ResNet as the generators (named as UnetGAN, ResGAN).
The three models could improve the image quality of CBCT and reduce the scattering artifacts while preserving the anatomical geometry of CBCT. For the chest test set, TCBCTNet achieved the best mean absolute error (MAE, 21.18±3.76 HU), better than 23.06±3.90 HU in UnetGAN and 22.47±3.57 HU in ResGAN. When applied to patients with breast cancer, TCBCTNet performance decreased, and MAE was 25.34±6.09 HU. Compared with rCT, sCT by TCBCTNet showed consistent dose distribution and subtle absolute dose differences between the target and the organ at risk. The 3D gamma pass rates were 98.98%±0.64% and 99.69%±0.22% at 2 mm/2% and 3 mm/3%, respectively. Ablation experiments confirmed that pCT and content loss played important roles in TCBCTNet.
High-quality sCT images could be synthesized from truncated low-dose CBCT and pCT by using the proposed TCBCTNet model. In addition, sCT could be used to accurately calculate the dose distribution for patients with breast cancer.
提出一种生成对抗网络（TCBCTNet），用于从截断的低剂量锥形束计算机断层扫描（CBCT）和计划计算机断层扫描（pCT）生成合成计算机断层扫描（sCT）。将sCT应用于乳腺癌患者的放射治疗剂量计算。
使用80例女性胸部患者的低剂量CBCT和pCT图像进行训练。使用20例胸部患者和20例乳腺癌患者的CBCT、pCT和重新计划CT（rCT）图像进行测试。所有患者均使用真空垫固定在相同姿势。CBCT图像按照快速胸部M20协议进行扫描，与标准胸部M20协议相比，投影帧数减少50%。在pCT和CBCT之间进行刚性配准，在rCT和CBCT之间进行变形配准。在TCBCTNet的训练阶段，使用通过模拟从完整CBCT图像中获得的截断CBCT图像。CBCT→CT生成器的输入是截断的CBCT和pCT，训练后将TCBCTNet应用于乳腺癌患者。通过解剖学和剂量学评估sCT的准确性，并与以U-Net和ResNet作为生成器的生成对抗网络（分别命名为UnetGAN、ResGAN）进行比较。
这三种模型都可以提高CBCT的图像质量，减少散射伪影，同时保留CBCT的解剖几何结构。对于胸部测试集，TCBCTNet实现了最佳平均绝对误差（MAE，21.18±3.76 HU），优于UnetGAN中的23.06±3.90 HU和ResGAN中的22.47±3.57 HU。当应用于乳腺癌患者时，TCBCTNet的性能下降，MAE为25.34±6.09 HU。与rCT相比，TCBCTNet生成的sCT显示出一致的剂量分布，靶区和危及器官之间的绝对剂量差异细微。在2 mm/2%和3 mm/3%时，3D伽马通过率分别为98.98%±0.64%和99.69%±0.22%。消融实验证实pCT和内容损失在TCBCTNet中起重要作用。
使用所提出的TCBCTNet模型可以从截断的低剂量CBCT和pCT合成高质量的sCT图像。此外，sCT可用于准确计算乳腺癌患者的剂量分布。
