SAM-guided structural consistency constraints for unsupervised MR-to-CT synthesis

MR-to-CT synthesis is critical for MRI-only radiotherapy. Conventional unsupervised methods lack structural consistency constraints, leading to unacceptable anatomical misalignments in synthesized CT images. To address this, we propose SAM-guided structural consistency constraints by integrating the Segment Anything Model into unsupervised MR-to-CT synthesis. We leverage SAM's cross-modal segmentation capability to enforce anatomical alignment between MR and synthesized CT images. During training, prompt augmentation is incorporated to enhance generalization across diverse anatomical structures. The proposed method is evaluated on a nasopharyngeal carcinoma dataset. The results demonstrate that our approach outperforms others on this dataset with MAE of 108.12 HU, PSNR of 24.24 dB, and SSIM of 0.753. The proposed SAM-guided method maintains exceptional anatomical consistency in synthesized CT images, as confirmed by quantitative analyses and dose distribution assessments. This work shows potential for supporting MRI-only radiotherapy by improving anatomical localization accuracy.