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

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.