  Currently, MRI-only radiotherapy (RT) eliminates some of the concerns about
using CT images in RT chains such as the registration of MR images to a
separate CT, extra dose delivery, and the additional cost of repeated imaging.
However, one remaining challenge is that the signal intensities of MRI are not
related to the attenuation coefficient of the biological tissue. This work
compares the performance of two state-of-the-art deep learning models; a
generative adversarial network (GAN) and a residual network (ResNet) for
synthetic CTs (sCT) generation from MR images. The brain MR and CT images of 86
participants were analyzed. GAN and ResNet models were implemented for the
generation of synthetic CTs from the 3D T1-weighted MR images using a six-fold
cross-validation scheme. The resulting sCTs were compared, considering the CT
images as a reference using standard metrics such as the mean absolute error
(MAE), peak signal-to-noise ratio (PSNR) and the structural similarity index
(SSIM). Overall, the ResNet model exhibited higher accuracy in relation to the
delineation of brain tissues. The ResNet model estimated the CT values for the
entire head region with an MAE of 114.1 HU compared to MAE=-10.9 HU obtained
from the GAN model. Moreover, both models offered comparable SSIM and PSNR
values, although the ResNet method exhibited a slightly superior performance
over the GAN method. We compared two state-of-the-art deep learning models for
the task of MR-based sCT generation. The ResNet model exhibited superior
results, thus demonstrating its potential to be used for the challenge of
synthetic CT generation in PET/MR AC and MR-only RT planning.
