A cycle generative adversarial network for improving the quality of four-dimensional cone-beam computed tomography images.
A cycle generative adversarial network for improving the quality of four-dimensional cone-beam computed tomography images.
复制标题
DOI：
10.1186/s13014-022-02042-1
复制
发表时间：
2022-04-07
期刊：
影响因子：
--
通讯作者：
Daida H
中科院分区：
文献类型：
--
作者：
Usui K;Ogawa K;Goto M;Sakano Y;Kyougoku S;Daida H
四维锥形束计算机断层扫描（4D-CBCT）可以可视化移动的肿瘤，因此如果使用4D-CBCT，可以改善自适应放射治疗（ART）。然而，4D-CBCT图像遭受严重的成像伪影。本研究的目的是研究使用循环生成对抗网络（CycleGAN）创建的合成4D-CBCT（sCT）图像进行肺癌ART。20例肺癌患者的非配对胸部4D-CBCT图像和四维多层螺旋CT（4D-MSCT）图像用于训练。CycleGAN模型生成的高质量sCT肺部图像在另外10个病例上进行了测试。计算平均误差和平均绝对误差以评估计算机断层扫描数量的变化。采用结构相似性指数测度（SSIM）和峰值信噪比（PSNR）对sCT和原始4D-CBCT图像进行比较。此外，使用sCT图像重新计算四个部分中剂量为48戈伊的体积调制弧治疗计划，并与4D-MSCT图像中观察到的理想剂量分布进行比较。生成的sCT图像具有较少的伪影，并且在sCT图像中清楚地观察到肺肿瘤区域。所有器官区域的平均值和平均绝对误差均接近0 Hounsfield单位。sCT图像的SSIM和PSNR结果分别显著改善约51%和18%。此外，伽马分析的结果得到了显著改善;使用sCT图像重新计算的剂量的通过率达到90%以上。此外，sCT图像的各器官剂量指数与4D-MSCT图像的各器官剂量指数一致，均在约5%范围内。CycleGAN增强了4D-CBCT图像的质量，使其与4D-MSCT图像相当。因此，临床实施基于sCT的ART治疗肺癌是可行的。
Four-dimensional cone-beam computed tomography (4D-CBCT) can visualize moving tumors, thus adaptive radiation therapy (ART) could be improved if 4D-CBCT were used. However, 4D-CBCT images suffer from severe imaging artifacts. The aim of this study is to investigate the use of synthetic 4D-CBCT (sCT) images created by a cycle generative adversarial network (CycleGAN) for ART for lung cancer. Unpaired thoracic 4D-CBCT images and four-dimensional multislice computed tomography (4D-MSCT) images of 20 lung-cancer patients were used for training. High-quality sCT lung images generated by the CycleGAN model were tested on another 10 cases. The mean and mean absolute errors were calculated to assess changes in the computed tomography number. The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) were used to compare the sCT and original 4D-CBCT images. Moreover, a volumetric modulation arc therapy plan with a dose of 48 Gy in four fractions was recalculated using the sCT images and compared with ideal dose distributions observed in 4D-MSCT images. The generated sCT images had fewer artifacts, and lung tumor regions were clearly observed in the sCT images. The mean and mean absolute errors were near 0 Hounsfield units in all organ regions. The SSIM and PSNR results were significantly improved in the sCT images by approximately 51% and 18%, respectively. Moreover, the results of gamma analysis were significantly improved; the pass rate reached over 90% in the doses recalculated using the sCT images. Moreover, each organ dose index of the sCT images agreed well with those of the 4D-MSCT images and were within approximately 5%. The proposed CycleGAN enhances the quality of 4D-CBCT images, making them comparable to 4D-MSCT images. Thus, clinical implementation of sCT-based ART for lung cancer is feasible.
登录
查看更多内容
影响因子：
3.8
作者：
Harms J;Lei Y;Wang T;Zhang R;Zhou J;Tang X;Curran WJ;Liu T;Yang X
通讯作者：
Yang X
DOI：
10.1016/j.ejmp.2020.11.007
发表时间：
2020-12-01
影响因子：
3.4
作者：
Eckl, Miriam;Hoppen, Lea;Fleckenstein, Jens
通讯作者：
Fleckenstein, Jens
影响因子：
4.4
作者：
Clark, Kenneth;Vendt, Bruce;Prior, Fred
通讯作者：
Prior, Fred
影响因子：
3.5
作者：
Shultz, David Benjamin;Diehn, Maximilian;Loo, Billy W., Jr.
通讯作者：
Loo, Billy W., Jr.
影响因子：
7.7
作者：
Liu, Yi;Hong Shangguan;Gui, Zhiguo
通讯作者：
Gui, Zhiguo