  A total of twenty paired CT and MR images were used in this study to
investigate two conditional generative adversarial networks, Pix2Pix, and Cycle
GAN, for generating synthetic CT images for Headand Neck cancer cases. Ten of
the patient cases were used for training and included such common artifacts as
dental implants; the remaining ten testing cases were used for testing and
included a larger range of image features commonly found in clinical head and
neck cases. These features included strong metal artifacts from dental
implants, one case with a metal implant, and one case with abnormal anatomy.
The original CT images were deformably registered to the mDixon FFE MR images
to minimize the effects of processing the MR images. The sCT generation
accuracy and robustness were evaluated using Mean Absolute Error (MAE) based on
the Hounsfield Units (HU) for three regions (whole body, bone, and air within
the body), Mean Error (ME) to observe systematic average offset errors in the
sCT generation, and dosimetric evaluation of all clinically relevant
structures. For the test set the MAE for the Pix2Pix and Cycle GAN models were
92.4 $\pm$ 13.5 HU, and 100.7 $\pm$ 14.6 HU, respectively, for the body region,
166.3 $\pm$ 31.8 HU, and 184 $\pm$ 31.9 HU, respectively, for the bone region,
and 183.7 $\pm$ 41.3 HU and 185.4 $\pm$ 37.9 HU for the air regions. The ME for
Pix2Pix and Cycle GAN were 21.0 $\pm$ 11.8 HU and 37.5 $\pm$ 14.9 HU,
respectively. Absolute Percent Mean/Max Dose Errors were less than 2% for the
PTV and all critical structures for both models, and DRRs generated from these
models looked qualitatively similar to CT generated DRRs showing these methods
are promising for MR-only planning.
