  To enable magnetic resonance (MR)-only radiotherapy and facilitate modelling
of radiation attenuation in humans, synthetic-CT (sCT) images need to be
generated. Considering the application of MR-guided radiotherapy and online
adaptive replanning, sCT generation should occur within minutes. This work aims
at assessing whether an existing deep learning network can rapidly generate sCT
images to be used for accurate MR-based dose calculations in the entire pelvis.
A study was conducted on data of 91 patients with prostate, rectal and cervical
cancer who underwent external beam radiotherapy acquiring both CT and MRI for
patients' simulation. Dixon reconstructed water, fat and in-phase images
obtained from a conventional dual gradient-recalled echo sequence were used to
generate sCT images. A conditional generative adversarial network (cGAN) was
trained in a paired fashion on 2D transverse slices of 32 prostate cancer
patients. The trained network was tested on the remaining patients to generate
sCT images. For 30 patients in the test set, dose recalculations of the
clinical plan were performed on sCT images. Dose distributions were evaluated
comparing voxel-based dose differences, gamma and dose-volume histogram (DVH)
analysis. The sCT generation required 5.6 s and 21 s for a single patient
volume on a GPU and CPU, respectively. On average, sCT images resulted in a
higher dose to the target of maximum 0.3%. Results suggest that accurate
MR-based dose calculation using sCT images generated with a cGAN trained on
prostate cancer patients is feasible for the entire pelvis. The sCT generation
was sufficiently fast to be integrated into an MR-guided radiotherapy workflow.
