  MRI-guided radiation treatment planning is widely applied because of its
superior soft-tissue contrast and no ionization radiation compared to CT-based
planning. In this regard, synthetic CT (sCT) images should be generated from
the patients MRI scans if radiation treatment planning is sought. Among the
different available methods for this purpose, the deep learning algorithms have
and do outperform their conventional counterparts. In this study, we
investigated the performance of some most popular deep learning architectures
including eCNN, U-Net, GAN, V-Net, and Res-Net for the task of sCT generation.
As a baseline, an atlas-based method is implemented to which the results of the
deep learning-based model are compared. A dataset consisting of 20
co-registered MR-CT pairs of the male pelvis is applied to assess the different
sCT production methods' performance. The mean error (ME), mean absolute error
(MAE), Pearson correlation coefficient (PCC), structural similarity index
(SSIM), and peak signal-to-noise ratio (PSNR) metrics were computed between the
estimated sCT and the ground truth (reference) CT images. The visual inspection
revealed that the sCTs produced by eCNN, V-Net, and ResNet, unlike the other
methods, were less noisy and greatly resemble the ground truth CT image. In the
whole pelvis region, the eCNN yielded the lowest MAE (26.03-8.85 HU) and ME
(0.82-7.06 HU), and the highest PCC metrics were yielded by the eCNN
(0.93-0.05) and ResNet (0.91-0.02) methods. The ResNet model had the highest
PSNR of 29.38-1.75 among all models. In terms of the Dice similarity
coefficient, the eCNN method revealed superior performance in major tissue
identification (air, bone, and soft tissue). All in all, the eCNN and ResNet
deep learning methods revealed acceptable performance with clinically tolerable
quantification errors.
