Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
J Appl Clin Med Phys. 2024 Oct;25(10):e14487. doi: 10.1002/acm2.14487. Epub 2024 Aug 26.
To develop a non-coplanar cone-beam computed tomography (CBCT) image reconstruction method using projections within a limited angle range for non-coplanar radiotherapy.
A generative adversarial network (GAN) was utilized to reconstruct non-coplanar CBCT images. Data from 40 patients with brain tumors and two head phantoms were used in this study. In the training stage, the generator of the GAN used coplanar CBCT and non-coplanar projections as the input, and an encoder with a dual-branch structure was utilized to extract features from the coplanar CBCT and non-coplanar projections separately. Non-coplanar CBCT images were then reconstructed using a decoder by combining the extracted features. To improve the reconstruction accuracy of the image details, the generator was adversarially trained using a patch-based convolutional neural network as the discriminator. A newly designed joint loss was used to improve the global structure consistency rather than the conventional GAN loss. The proposed model was evaluated using data from eight patients and two phantoms at four couch angles (±45°, ±90°) that are most commonly used for brain non-coplanar radiotherapy in our department. The reconstructed accuracy was evaluated by calculating the root mean square error (RMSE) and an overall registration error ε, computed by integrating the rigid transformation parameters.
In both patient data and phantom data studies, the qualitative and quantitative metrics results indicated that ± 45° couch angle models performed better than ±90° couch angle models and had statistical differences. In the patient data study, the mean RMSE and ε values of couch angle at 45°, -45°, 90°, and -90° were 58.5 HU and 0.42 mm, 56.8 HU and 0.41 mm, 73.6 HU and 0.48 mm, and 65.3 HU and 0.46 mm, respectively. In the phantom data study, the mean RMSE and ε values of couch angle at 45°, -45°, 90°, and -90° were 91.2 HU and 0.46 mm, 95.0 HU and 0.45 mm, 114.6 HU and 0.58 mm, and 102.9 HU and 0.52 mm, respectively.
The results show that the reconstructed non-coplanar CBCT images can potentially enable intra-treatment three-dimensional position verification for non-coplanar radiotherapy.
为非共面放射治疗开发一种使用有限角度范围内投影的非共面锥形束 CT（CBCT）图像重建方法。
利用生成对抗网络（GAN）对非共面 CBCT 图像进行重建。本研究使用了 40 名脑肿瘤患者和两个头部体模的数据。在训练阶段，GAN 的生成器将共面 CBCT 和非共面投影作为输入，使用具有双分支结构的编码器分别从共面 CBCT 和非共面投影中提取特征。然后，使用解码器通过组合提取的特征来重建非共面 CBCT 图像。为了提高图像细节的重建精度，使用基于补丁的卷积神经网络作为鉴别器对生成器进行对抗训练。使用新设计的联合损失来提高全局结构一致性，而不是传统的 GAN 损失。使用来自我们部门最常用于脑非共面放射治疗的四个治疗床角度（±45°，±90°）的 8 名患者和两个体模的数据来评估所提出的模型。通过计算均方根误差（RMSE）和整体注册误差 ε 来评估重建准确性，ε 通过积分刚体变换参数计算得到。
在患者数据和体模数据研究中，定性和定量指标结果表明，±45°治疗床角度模型的性能优于±90°治疗床角度模型，且具有统计学差异。在患者数据研究中，45°、-45°、90°和-90°治疗床角度的 RMSE 和 ε 的平均值分别为 58.5HU 和 0.42mm、56.8HU 和 0.41mm、73.6HU 和 0.48mm、65.3HU 和 0.46mm。在体模数据研究中，45°、-45°、90°和-90°治疗床角度的 RMSE 和 ε 的平均值分别为 91.2HU 和 0.46mm、95.0HU 和 0.45mm、114.6HU 和 0.58mm、102.9HU 和 0.52mm。
结果表明，重建的非共面 CBCT 图像可能能够为非共面放射治疗提供治疗中三维位置验证。
