Room: Track 1
Purpose: investigate the effect of different MR sequences on the accuracy of sCT generation and to proceed dosimetric evaluation on sCTs from different MR sequences in head and neck.
Methods: kinds of MR sequence images (T1, T2, T1C and T1DixonC) and CT images were collected from 45 patients with nasopharyngeal carcinoma. 30 patients were randomly selected to train five mapping models (four single-channel models and one 4-channel model, each channel used an MR sequence image as input) with conditional generative adversarial network (cGAN). The remaining 15 patients in the testing set were used for evaluation. All predicted sCTs were evaluated in the image domain and imported into TPS for dose recalculation. The dose distributions of sCT were evaluated by MAE and gamma pass rate (2mm/2%) for different volumes of interest, different tissue types, different OARs and several dose-volume histogram (DVH) points.
Results: all the five models, the 4-channel model achieves highest accuracy in both sCT image quality and dosimetric accuracy than any single-channel models. Among the four single-channel models, the T1-weighted MR model has the best performance. The dose comparison of the five models in target and OARs show that the MAE values of target are all less than 1% of prescription dose, and the MAE values of parotids, lymph nodes, brainstem and spinal cord are all less than 0.6%. The differences for dose-volume criteria of PTVs and OARs from all five models are all within -1.5%~1.0%.
Conclusion: sCT generation models based on multi-sequence MR images were successfully established with cGAN for head and neck. Both the dosimetric evaluation and sCT comparison show that the performance of 4-channel model and T1 model is slightly better than other models. All dosimetric evaluations suggest that the sCTs generated by all sequence models can be applied to the process of MR-only radiotherapy.
Funding Support, Disclosures, and Conflict of Interest: The present work was supported by the Natural Science Foundation of Guangdong Province, China (No. 2018A0303100020), National Key R&D Program of China (Grant NO. 2017YFC0113203) and the National Natural Science Foundation of China (Grant NO. 11805292, 81601577and 81571771).