Session Item

In the context of MR-only radiotherapy workflow, several deep learning methods (DLMs) have been developed for synthetic-CT (sCT) generation from MR images. The Pix2Pix DLM (a conditional generative adversarial network [cGAN]) can be applied on the 3 MRI views (transverse, sagittal and coronal) and not only on the axial view. The aim of this study was to compare the sCTs resulting from the 2D+ Pix2Pix model (in the 3 views) and the 2D Pix2Pix model (axial view) for prostate MRI-only radiotherapy. 

Prostate CT and MR images were acquired in treatment position for 39 patients. MR acquisitions, using T2/T1-weighted TrueFISP sequences, were performed with an MRI-linac device (MRIdian, Viewray, 0.35T). 2D+ method consists of generating 3 sCTs (according to each view) per patient, and combined in one sCT by using the median voxel value. sCTs generated by the 2D Pix2Pix model (axial view) were compared to sCTs generated with the 2D+ Pix2Pix model. For both of these methods, the perceptual loss function, a ResNet 9 blocks generator, a PatchGAN discriminator, and Adam optimizer were used. The evaluation was performed on a 5-fold cross validation using 30 patient images for training and 9. Finally, both sCT were compared to the original CT from a voxel-wise comparison with the mean absolute error (MAE) in Hounsfield units (HU), mean absolute percentage error (MAPE) in % , and peak signal to noise ratio (PSNR) in dB. The Wilcoxon test was used to compare the results obtained with the 2D+ model to those obtained with the 2D model. Significant differences were considered for p-value<0.05.

Table 1 presents the results of MAE, MAPE and PSNR for the two methods. For the body and the bones, significantly lower MAE and MAPE results were found with the 2D+ Pix2Pix model, compared to the 2D Pix2Pix model. For the body and the bones, significantly higher PSNR results were found with the 2D+ Pix2Pix model, compared to the 2D Pix2Pix model.
sCTs generated from 2D+ Pix2Pix model were less impacted by inter-slices artefacts (Figure 1) than sCTs generated by 2D Pix2Pix model. 

Table 1: MAE, ME and PSNR (mean ± standard deviation) results in the body and the bones for 39 patients

*Significant differences were considered at p-value<0.05.

In order to generate sCT from prostate MRI, the 2D+ Pix2Pix model allows to generate sCT with less image uncertainties than the 2D Pix2Pix model. The next step will be a dosimetric evaluation of sCTs generated by the 2D+ Pix2Pix model.