AI 中文总结
针对内部断层扫描中投影截断致逆问题不适定及现有方法泛化性不足等挑战，提出FORCE-Interior框架，结合全视野测量约束初始化与每步数据一致性，在不同截断ROI尺寸上提升重建质量并保持投影域一致性。
AI 中文摘要
内部断层扫描从截断投影测量中重建感兴趣区域（ROI）。传统方法直接应用时，投影截断会使逆问题严重不适定，导致解不唯一、过度平滑和截断伪影。现有基于学习的内部CT方法在不同截断模式等方面泛化性不足，当前基于生成模型的重建方法也不适用于内部断层扫描。为此提出FORCE-Interior，它结合全视野测量约束初始化与每步数据一致性，实验表明其在更严重截断的ROI尺寸上重建质量提高，在最大尺寸上也有竞争力，且保持投影域一致性。
英文摘要
Interior tomography reconstructs a region of interest (ROI) from truncated projections, an ill-posed problem with non-unique solutions and truncation-induced bias. Existing deep-learning methods can be sensitive to changes in ROI geometry and noise, while expressive generative priors may produce measurement-inconsistent content without measurement constraints. We propose FORCE-Interior, a training-free adaptation of a pretrained Poisson-flow generative prior to interior CT. A full-field-of-view (FOV) OS-SART warm start avoids forcing all measured attenuation into the ROI, and truncation-mask-aware OS-SART updates enforce data consistency throughout sampling. In our experiment, FORCE-Interior achieves the best PSNR, SSIM, and LPIPS at the two more severely truncated synthetic ROI sizes and competitive performance at the largest ROI, while maintaining low projection-domain residuals. These findings support the measurement-consistent adaptation of a reusable generative CT prior, while further clinical and patient-level validation remains necessary.
