Cycle-consistent Generative Adversarial Network Synthetic CT for MR-only Adaptive Radiation Therapy on MR-Linac
Purpose: This study assesses the effectiveness of Deep Learning (DL) for creating synthetic CT (sCT) images in MR-guided adaptive radiation therapy (MRgART). Methods: A Cycle-GAN model was trained with MRI and CT scan slices from MR-LINAC treatments, generating sCT volumes. The analysis involved retrospective treatment plan data from patients with various tumors. sCT images were compared with standard CT scans using mean absolute error in Hounsfield Units (HU) and image similarity metrics (SSIM, PSNR, NCC). sCT volumes were integrated into a clinical treatment system for dosimetric re-evaluation. Results: The model, trained on 8405 frames from 57 patients and tested on 357 sCT frames from 17 patients, showed sCTs comparable to dCTs in electron density and structural similarity with MRI scans. The MAE between sCT and dCT was 49.2 +/- 13.2 HU, with sCT NCC exceeding dCT by 0.06, and SSIM and PSNR at 0.97 +/- 0.01 and 19.9 +/- 1.6 respectively. Dosimetric evaluations indicated minimal differences between sCTs and dCTs, with sCTs showing better air-bubble reconstruction. Conclusions: DL-based sCT generation on MR-Linacs is accurate for dose calculation and optimization in MRgART. This could facilitate MR-only treatment planning, enhancing simulation and adaptive planning efficiency on MR-Linacs.
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