ArticleJournal of applied clinical medical physics2026
Knowledge-based deep residual U-Net (DRU) for synthetic CT generation using a single MR volume for frameless radiosurgery.
Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Knowledge-based deep residual U-Net (DRU) for synthetic CT generation using a single MR volume for frameless radiosurgery.Journal of applied clinical medical physics · 2026Article
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Authors and funding
9 authors.
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Abstract
purposeTo develop a knowledge-based deep model for sCT generation from a single MR volume in LINAC-based frameless SRS, enabling the MR-only workflow without extra CT simulation.
methodsA total of 139 patients were included in the study, with 120 used for training and 19 for testing. A Deep Residual U-Net(DRU) was developed to generate sCT from patient-specific high-resolution T1 + Contrast 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, thereby creating a full sCT
resultsIn the test set, the generated sCT (statistics are reported as mean ± standard deviation) achieved a PSNR(Peak Signal-to-Noise Ratio) of 75 ± 4 dB, Structural Similarity Index (SSIM) of 0.99 ± 0.01, root mean square error (RMSE) of 11.9 ± 5.8 HU, and mean average error (MAE) of 1.4 ± 0.8 HU for brain tissues. When comparing sCT
conclusionThis 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 without simulation CT scan effectively supports frameless SRS planning and integrates seamlessly into current clinical workflows.
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