  Electron density maps must be accurately estimated to achieve valid dose
calculation in MR-only radiotherapy. The goal of this study is to assess
whether two deep learning models, the conditional generative adversarial
network (cGAN) and the cycle-consistent generative adversarial network
(cycleGAN), can generate accurate abdominal synthetic CT (sCT) images from
0.35T MR images for MR-only liver radiotherapy. A retrospective study was
performed using CT images and 0.35T MR images of 12 patients with liver (n=8)
and non-liver abdominal (n=4) cancer. CT images were deformably registered to
the corresponding MR images to generate deformed CT (dCT) images for treatment
planning. Both cGAN and cycleGAN were trained using MR and dCT transverse
slices. Four-fold cross-validation testing was conducted to generate sCT images
for all patients. The HU prediction accuracy was evaluated by voxel-wise
similarity metric between each dCT and sCT image for all 12 patients. dCT-based
and sCT-based dose distributions were compared using gamma and dose-volume
histogram (DVH) metric analysis for 8 liver patients. sCTcycleGAN achieved the
average mean absolute error (MAE) of 94.1 HU, while sCTcGAN achieved 89.8 HU.
In both models, the average gamma passing rates within all volumes of interest
were higher than 95% using a 2%, 2 mm criterion, and 99% using a 3%, 3 mm
criterion. The average differences in the mean dose and DVH metrics were within
+/-0.6% for the planning target volume and within +/-0.15% for evaluated organs
in both models. Results demonstrated that abdominal sCT images generated by
both cGAN and cycleGAN achieved accurate dose calculation for 8 liver
radiotherapy plans. sCTcGAN images had smaller average MAE and achieved better
dose calculation accuracy than sCTcyleGAN images. More abdominal patients will
be enrolled in the future to further evaluate two models.
