Purpose: A kV digital radiograph (kV-DR) using ExacTrac® X-ray source (BrainLAB AG, Munich, Germany) undergoes degradation of image quality due to radiotherapy accessories equipped for patient immobilization. We proposed a deep learning model based on cycleGAN image synthesis to improve the image quality of ExacTrac kV-DR.
Methods: A total of 2574 paired kV-DRs and digitally reconstructed radiograph (DRR) were obtained from stereotactic radiotherapy (SRT/SRS) treatments for patients with brain or spine cancer. The DRR-like synthetic kV-DRs were generated from kV-DRs directly obtaining from ExacTrac using a deep learning-based image synthesis network. 2166 and 234 of kV-DR and DRR pairs were used for training for testing, and 92 and 82 pairs were used for testing from brain and spine cases, respectively. The images were normalized into [0, 1], and then the order of the paired data cases was shuffled to prevent the overfitting in network training. The mean absolute error (MAE), the root-mean-square error (RMSE), and the peak signal-to-noise ratio (PSNR) were used to quantify the image quality of the synthetic kV-DR.
Results: The synthetic kV-DR (MAE⁼0.26±0.08, RMSE⁼0.33±0.09, and PSNR⁼9.83±0.33 dB) showed a better image quality compared to ExacTrac kV-DRs (MAE⁼0.34±0.14, RMSE⁼0.39±0.14, and PSNR⁼8.62±3.07 dB). The average MAE, RMSE, and PSNR values were 0.21±0.06, 0.29±0.07, and 11.06±8.45 dB for the brain cases and 0.31±0.07, 0.39±0.08, and 9.83±2.42 dB for spine cases, respectively.
Conclusion: We successfully suppressed the artifacts in the kV-DRs originated from radiotherapy accessories and generated a DRR-like synthetic kV-DRs from ExacTrac based on deep learning network. The proposed model can improve the tumor localization accuracy for SRS/SRT radiotherapy.
Funding Support, Disclosures, and Conflict of Interest: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1F1A1075741).
Image Guidance, Digital Imaging, Image Processing