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Purpose: This study assesses the effectiveness of Deep Learning (DL) forcreating synthetic CT (sCT) images in MR-guided adaptive radiation therapy(MRgART).  Methods: A Cycle-GAN model was trained with MRI and CT scan slices fromMR-LINAC treatments, generating sCT volumes. The analysis involvedretrospective treatment plan data from patients with various tumors. sCT imageswere compared with standard CT scans using mean absolute error in HounsfieldUnits (HU) and image similarity metrics (SSIM, PSNR, NCC). sCT volumes wereintegrated into a clinical treatment system for dosimetric re-evaluation.  Results: The model, trained on 8405 frames from 57 patients and tested on 357sCT frames from 17 patients, showed sCTs comparable to dCTs in electron densityand 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 minimaldifferences between sCTs and dCTs, with sCTs showing better air-bubblereconstruction.  Conclusions: DL-based sCT generation on MR-Linacs is accurate for dosecalculation and optimization in MRgART. This could facilitate MR-only treatmentplanning, enhancing simulation and adaptive planning efficiency on MR-Linacs.

Cycle-consistent Generative Adversarial Network Synthetic CT for MR-only Adaptive Radiation Therapy on MR-Linac