Synthetic CT generation from CBCT based on structural constraint cycle-EEM-GAN

Abstract

Objective. Cone beam CT (CBCT) typically has severe image artifacts and inaccurate HU values, which limits its application in radiation medicines. Scholars have proposed the use of cycle consistent generative adversarial network (Cycle-GAN) to address these issues. However, the generation quality of Cycle-GAN needs to be improved. This issue is exacerbated by the inherent size discrepancies between pelvic CT scans from different patients, as well as varying slice positions within the same patient, which introduce a scaling problem during training. Approach. We introduced the Enhanced Edge and Mask (EEM) approach in our structural constraint Cycle-EEM-GAN. This approach is designed to not only solve the scaling problem but also significantly improve the generation quality of the synthetic CT images. Then data from sixty pelvic patients were used to investigate the generation of synthetic CT (sCT) from CBCT. Main results. The mean absolute error (MAE), the root mean square error (RMSE), the peak signal to noise ratio (PSNR), the structural similarity index (SSIM), and spatial nonuniformity (SNU) are used to assess the quality of the sCT generated from CBCT. Compared with CBCT images, the MAE improved from 53.09 to 37.74, RMSE from 185.22 to 146.63, SNU from 0.38 to 0.35, PSNR from 24.68 to 32.33, SSIM from 0.624 to 0.981. Also, the Cycle-EEM-GAN outperformed Cycle-GAN in terms of visual evaluation and loss. Significance. Cycle-EEM-GAN has improved the quality of CBCT images, making the structural details clear while prevents image scaling during the generation process, so that further promotes the application of CBCT in radiotherapy.