MR to CT synthesis with multicenter data in the pelvic area using a conditional generative adversarial network

The establishment of an MRI-only workflow in radiotherapy depends on the ability to generate an accurate synthetic CT (sCT) for dose calculation. Previously proposed methods have used a Generative Adversarial Network (GAN) for fast sCT generation in order to simplify the clinical workflow and reduces uncertainties. In the current paper we use a conditional Generative Adversarial Network (cGAN) framework called pix2pixHD to create a robust model prone to multicenter data. This study included T2-weighted MR and CT images of 19 patients in treatment position from 3 different sites. The cGAN was trained on 2D transverse slices of 11 patients from 2 different sites. Once trained, the network was used to generate sCT images of 8 patients coming from a third site. The Mean Absolute Errors (MAE) for each patient were evaluated between real and synthetic CTs. A radiotherapy plan was optimized on the sCT series and re-calculated on CTs to assess the dose distribution in terms of voxel-wise dose difference and Dose Volume Histograms (DVH) analysis. It takes on average of 7.5s