Human-Guided Shading Artifact Suppression in CBCT-to-MDCT Translation via Schrödinger Bridge with Conditional Diffusion

We present a novel framework for CBCT-to-MDCT translation, grounded in the Schrödinger Bridge (SB) formulation, which integrates GAN-derived priors with human-guided conditional diffusion. Unlike conventional GANs or diffusion models, our approach explicitly enforces boundary consistency between CBCT inputs and pseudo-targets, ensuring both anatomical fidelity and perceptual controllability. Binary human feedback is incorporated via classifier-free guidance (CFG), effectively steering the generative process toward clinically preferred outcomes. Through iterative refinement and tournament-based preference selection, the model internalizes human preferences without relying on a reward model. Subtraction image visualizations reveal that the proposed method selectively attenuates shading artifacts in key anatomical regions while preserving fine structural detail. Quantitative evaluations further demonstrate superior performance across RMSE, SSIM, LPIPS, and Dice metrics on clinical datasets—outperforming prior GAN- and fine-tuning-based feedback methods—while requiring only 10 sampling steps. The present validation was conducted exclusively on head and neck CBCT datasets, which represent the most clinically relevant use case for dental and craniofacial imaging. These findings underscore the effectiveness and efficiency of our framework for real-time, preference-aligned medical image translation.