Methods: The DSBM learns the nonlinear diffusion processes between MR and CT data distributions. Unlike traditional diffusion models (DMs), which start synthesis from a Gaussian distribution, DSBM starts from the prior distribution, enabling more direct and efficient synthesis. The model was trained on 46 head-and-neck (HN) MR-CT pairs and 77 brain tumor MR-CT pairs, with 8 and 10 scans used for testing, respectively. Comprehensive evaluations were conducted at both image and dosimetric levels, using metrics such as mean absolute error (MAE), Dice score, voxel-wise proton dose differences, gamma pass rates of clinical plans, and typical dose indices.
Results: For the HN dataset, DSBM achieved a lower MAE of 72.42
9.78 Hounsfield unit (HU) compared to 77.72 9.11 HU with the best baseline approach, and a higher Dice score for bone of 83.32 3.25% compared to 82.55 3.62%, indicating superior anatomical accuracy. Dosimetric evaluations showed a 1%/1 mm gamma pass rate of 95.85 2.99%, surpassing the 95.25 3.09% achieved by the baseline model. For the brain tumor dataset, DSBM outperformed the baseline with an MAE of 91.73 6.86 HU compared to 103.25 9.58 HU, and a Dice score for bone of 82.85 3.88% compared to 81.27 4.59%. DSBM also demonstrated a higher 1%/1 mm gamma pass rate of 97.93
1.82%, confirming its robustness across different anatomical regions. Notably, DSBM achieved these results with very few number of neural function evaluation steps, significantly improving computational efficiency compared to standard DMs.
Conclusions: The DSBM demonstrates superior performance over traditional image synthesis methods in MR-based proton treatment planning. Its ability to generate high-quality sCT images with enhanced speed and accuracy highlights its potential as a valuable and efficient tool in various radiotherapy clinical scenarios.