用分割引导和不确定性量化提升质子治疗中CBCT转CT的精度与可信度。

Accurate dose calculations in proton therapy rely on high-quality CT images. While planning CTs (pCTs) serve as a reference for dosimetric planning, Cone Beam CT (CBCT) is used throughout Adaptive Radiotherapy (ART) to generate sCTs for improved dose calculations. Despite its lower cost and reduced radiation exposure advantages, CBCT suffers from severe artefacts and poor image quality, making it unsuitable for precise dosimetry. Deep learning-based CBCT-to-CT translation has emerged as a promising approach. Still, existing methods often introduce anatomical inconsistencies and lack reliable uncertainty estimates, limiting their clinical adoption. To bridge this gap, we propose STF-RUE, a novel framework integrating two key components. First, STF, a segmentation-guided CBCT-to-CT translation method that enhances anatomical consistency by leveraging segmentation priors extracted from pCTs. Second, RUE, a conformal prediction method that augments predicted CTs with pixel-wise conformal prediction intervals, providing clinicians with robust reliability indicator. Comprehensive experiments using UNet++ and Fast-DDPM on two benchmark datasets demonstrate that STF-RUE significantly improves translation accuracy, as measured by a novel soft-tissue-focused metric designed for precise dose computation. Additionally, STF-RUE provides better-calibrated uncertainty sets for synthetic CT, reinforcing trust in synthetic CTs. By addressing both anatomical fidelity and uncertainty quantification, STF-RUE marks a crucial step toward safer and more effective adaptive proton therapy. Code is available at https://anonymous.4open.science/r/cbct2ct_translation-B2D9/.