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.
Med Biol Eng Comput. 2023 Jul;61(7):1757-1772. doi: 10.1007/s11517-023-02809-y. Epub 2023 Mar 10.
This study aimed to inpaint the truncated areas of CT images by using generative adversarial networks with gated convolution (GatedConv) and apply these images to dose calculations in radiotherapy. CT images were collected from 100 patients with esophageal cancer under thermoplastic membrane placement, and 85 cases were used for training based on randomly generated circle masks. In the prediction stage, 15 cases of data were used to evaluate the accuracy of the inpainted CT in anatomy and dosimetry based on the mask with a truncated volume covering 40% of the arm volume, and they were compared with the inpainted CT synthesized by U-Net, pix2pix, and PConv with partial convolution. The results showed that GatedConv could directly and effectively inpaint incomplete CT images in the image domain. For the results of U-Net, pix2pix, PConv, and GatedConv, the mean absolute errors for the truncated tissue were 195.54, 196.20, 190.40, and 158.45 HU, respectively. The mean dose of the planning target volume, heart, and lung in the truncated CT was statistically different (p < 0.05) from those of the ground truth CT ([Formula: see text]). The differences in dose distribution between the inpainted CT obtained by the four models and [Formula: see text] were minimal. The inpainting effect of clinical truncated CT images based on GatedConv showed better stability compared with the other models. GatedConv can effectively inpaint the truncated areas with high image quality, and it is closer to [Formula: see text] in terms of image visualization and dosimetry than other inpainting models.
本研究旨在使用具有门控卷积（GatedConv）的生成对抗网络来修复 CT 图像的截断区域，并将这些图像应用于放射治疗中的剂量计算。从 100 名接受热塑膜放置的食管癌患者中收集 CT 图像，根据随机生成的圆形掩模，使用 85 个病例进行训练。在预测阶段，使用 15 个病例的数据评估基于截断体积覆盖臂体积 40%的掩模的解剖和剂量学中修复的 CT 的准确性，并将其与 U-Net、pix2pix 和具有部分卷积的 PConv 合成的修复 CT 进行比较。结果表明，GatedConv 可以直接有效地在图像域中修复不完整的 CT 图像。对于 U-Net、pix2pix、PConv 和 GatedConv 的结果，截断组织的平均绝对误差分别为 195.54、196.20、190.40 和 158.45 HU。在截断 CT 中，计划靶区、心脏和肺的平均剂量与真实 CT 相比存在统计学差异（p<0.05）。四个模型生成的修复 CT 和[Formula: see text]之间的剂量分布差异最小。基于 GatedConv 的临床截断 CT 图像的修复效果与其他模型相比更稳定。GatedConv 可以有效地修复具有高质量图像的截断区域，并且在图像可视化和剂量学方面比其他修复模型更接近[Formula: see text]。
