基于双链CycleGAN 的锥形束 CT 图像合成。

Synthetic CT generation from CBCT using double-chain-CycleGAN.

机构信息

Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin, 150080, China.

Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China.

出版信息

Comput Biol Med. 2023 Jul;161:106889. doi: 10.1016/j.compbiomed.2023.106889. Epub 2023 Apr 3.

DOI:10.1016/j.compbiomed.2023.106889

PMID:37244147

Abstract

PURPOSE

Cone-beam CT (CBCT) has the advantage of being less expensive, lower radiation dose, less harm to patients, and higher spatial resolution. However, noticeable noise and defects, such as bone and metal artifacts, limit its clinical application in adaptive radiotherapy. To explore the potential application value of CBCT in adaptive radiotherapy, In this study, we improve the cycle-GAN's backbone network structure to generate higher quality synthetic CT (sCT) from CBCT.

METHOD

An auxiliary chain containing a Diversity Branch Block (DBB) module is added to CycleGAN's generator to obtain low-resolution supplementary semantic information. Moreover, an adaptive learning rate adjustment strategy (Alras) function is used to improve stability in training. Furthermore, Total Variation Loss (TV loss) is added to generator loss to improve image smoothness and reduce noise.

RESULTS

Compared to CBCT images, the Root Mean Square Error (RMSE) dropped by 27.97 from 158.49. The Mean Absolute Error (MAE) of the sCT generated by our model improved from 43.2 to 32.05. The Peak Signal-to-Noise Ratio (PSNR) increased by 1.61 from 26.19. The Structural Similarity Index Measure (SSIM) improved from 0.948 to 0.963, and the Gradient Magnitude Similarity Deviation (GMSD) improved from 12.98 to 9.33. The generalization experiments show that our model performance is still superior to CycleGAN and respath-CycleGAN.

摘要

目的

锥形束 CT（CBCT）具有成本低、辐射剂量低、对患者伤害小、空间分辨率高等优点。但明显的噪声和缺陷，如骨和金属伪影，限制了其在自适应放疗中的临床应用。为了探索 CBCT 在自适应放疗中的潜在应用价值，本研究改进了Cycle-GAN 的骨干网络结构，从 CBCT 生成更高质量的合成 CT（sCT）。

方法

在 CycleGAN 的生成器中添加包含多样性分支块（DBB）模块的辅助链，以获取低分辨率的补充语义信息。此外，采用自适应学习率调整策略（Alras）函数提高训练稳定性。此外，在生成器损失中添加全变差损失（TV 损失）以提高图像平滑度并减少噪声。

结果

与 CBCT 图像相比，均方根误差（RMSE）从 158.49 下降到 127.97。我们模型生成的 sCT 的平均绝对误差（MAE）从 43.2 提高到 32.05。峰值信噪比（PSNR）从 26.19 增加到 27.81。结构相似性指数度量（SSIM）从 0.948 提高到 0.963，梯度幅度相似性偏差（GMSD）从 12.98 提高到 9.33。泛化实验表明，我们的模型性能仍然优于 CycleGAN 和 respath-CycleGAN。