Knowledge-distilled diffusion models for improving cone-beam CT image quality with meta-learning under imbalanced data.

BACKGROUND: Adaptive radiation therapy (ART) relies on daily cone-beam CT (CBCT), yet its limited image quality hinders accurate dose calculation, particularly under substantial anatomical changes. PURPOSE: To overcome the clinical challenge of scarce paired planning CT (pCT) data versus abundant unpaired CBCTs, we propose a framework driven by two core components: knowledge distillation and gradient-based meta-guidance. METHODS: The knowledge distillation strategy enables the model to leverage the vast unpaired dataset. Complementing this, the meta-guidance mechanism stabilizes training by dynamically updating the weight of each unpaired sample; it assigns higher importance to pseudo-labels that align with trusted supervised gradients, effectively filtering out noise. Our method was evaluated on a cohort of 99 breast cancer patients (with 19 reserved for testing) and further evaluated on a public dataset to assess the generalization capability. RESULTS: The proposed approach demonstrated superior performance, achieving the best quantitative metrics (MAE 13.22 HU, SSIM 0.9516, PSNR 30.35 dB). It significantly outperformed representative supervised, unsupervised, and standard distillation baselines ( p < 0.01 ). Ablation studies confirm that our method enhances image quality while preserving the patient's daily anatomy by minimizing feature hallucination. CONCLUSIONS: By uniquely combining knowledge distillation with meta-guidance, our method advances the frontier of high-quality synthetic CT, enabling more robust and adaptive ART workflows.