Cycle-consistent Generative Adversarial Network Synthetic CT for MR-only
  Adaptive Radiation Therapy on MR-Linac
Gabriel L. Asher Bassem I. Zaki Gregory A. Russo Gobind S. Gill Charles R. Thomas Temiloluwa O. Prioleau Rongxiao Zhang Brady Hunt
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.