Author: Justus Adamson, John Ginn, Yongbok Kim, Ke Lu, Trey Mullikin, Xiwen Shu, Chunhao Wang, Zhenyu Yang, Jingtong Zhao 👨🔬
Affiliation: Duke University, Duke Kunshan University 🌍
Purpose:
To develop a knowledge-based deep model for synthetic CT (sCT) generation from a single MR volume in frameless radiosurgery (SRS), eliminating the need for CT simulation prior to the SRS delivery.
Methods:
A total of 139 patients were included in the study, with 120 patients used for training and 19 for testing. A Deep Residual U-Net (DRU) model was developed to generate sCT from patient-specific high-resolution T1+C MR volume, complemented by a healthy brain CT volume from the Visible Human Project that provides CT-specific anatomical knowledge. To simulate treatment conditions, a template immobilization mask was deformed to align with the patient-specific sCT anatomy, creating a full sCTF volume. Four metrics, including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), root mean square error (RMSE) and mean absolute error (MAE) were derived to evaluate HU accuracy of sCT in reference to the ground-truth CT without immobilization masks. Multi-target SRS plans, developed with VMAT technique, were recalculated within sCTF volumes to produce simulated dose distributions, which were compared with clinical plan dose distributions using the mean dose difference in the planning target volume (PTV) and gamma index evaluation.
Results:
In the test set, the generated sCT achieved a PSNR of 75.40 ± 3.58 dB, SSIM of 0.99 ± 0.01, RMSE of 11.88 ± 5.82 HU, and MAE of 1.38 ± 0.81 HU for brain tissues. Gamma index passing rates were 95.77 ± 4.17% for the entire volume and 84.36 ± 14.95% within PTVs, using 3%/1 mm/15% threshold criteria. The mean PTV dose difference averaged -2.32 ± 1.48%, with most discrepancies below -1.0%.
Conclusion:
This study successfully demonstrated the generation and validation of sCT images from single-modality MRI using a knowledge-based deep model. The results confirm that single-modality MRI effectively supports frameless SRS and integrates seamlessly into current clinical workflows.