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A total of twenty paired CT and MR images were used in this study toinvestigate two conditional generative adversarial networks, Pix2Pix, and CycleGAN, for generating synthetic CT images for Headand Neck cancer cases. Ten ofthe patient cases were used for training and included such common artifacts asdental implants; the remaining ten testing cases were used for testing andincluded a larger range of image features commonly found in clinical head andneck cases. These features included strong metal artifacts from dentalimplants, one case with a metal implant, and one case with abnormal anatomy.The original CT images were deformably registered to the mDixon FFE MR imagesto minimize the effects of processing the MR images. The sCT generationaccuracy and robustness were evaluated using Mean Absolute Error (MAE) based onthe Hounsfield Units (HU) for three regions (whole body, bone, and air withinthe body), Mean Error (ME) to observe systematic average offset errors in thesCT generation, and dosimetric evaluation of all clinically relevantstructures. For the test set the MAE for the Pix2Pix and Cycle GAN models were92.4 $\pm$ 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 forPix2Pix 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 thePTV and all critical structures for both models, and DRRs generated from thesemodels looked qualitatively similar to CT generated DRRs showing these methodsare promising for MR-only planning.

Comparison of Patch-Based Conditional Generative Adversarial Neural Net  Models with Emphasis on Model Robustness for Use in Head and Neck Cases for  MR-Only planning