Photon-counting CT (PCCT) provides superior image quality with higher spatial resolution and lower noise compared to conventional energy-integrating CT (EICT), but its limited clinical availability restricts large-scale research and clinical deployment. To bridge this gap, we propose SUMI, a simulated degradation-to-enhancement method that learns to reverse realistic acquisition artifacts in low-quality EICT by leveraging high-quality PCCT as reference. Our central insight is to explicitly model realistic acquisition degradations, transforming PCCT into clinically plausible lower-quality counterparts and learning to invert this process. The simulated degradations were validated for clinical realism by board-certified radiologists, enabling faithful supervision without requiring paired acquisitions at scale. As outcomes of this technical contribution, we: (1) train a latent diffusion model on 1,046 PCCTs, using an autoencoder first pre-trained on both these PCCTs and 405,379 EICTs from 145 hospitals to extract general CT latent features that we release for reuse in other generative medical imaging tasks; (2) construct a large-scale dataset of over 17,316 publicly available EICTs enhanced to PCCT-like quality, with radiologist-validated voxel-wise annotations of airway trees, arteries, veins, lungs, and lobes; and (3) demonstrate substantial improvements: across external data, SUMI outperforms state-of-the-art image translation methods by 15% in SSIM and 20% in PSNR, improves radiologist-rated clinical utility in reader studies, and enhances downstream top-ranking lesion detection performance, increasing sensitivity by up to 15% and F1 score by up to 10%. Our results suggest that emerging imaging advances can be systematically distilled into routine EICT using limited high-quality scans as reference.

翻译：光子计数CT（PCCT）相比传统能量积分CT（EICT）具有更高的空间分辨率和更低的噪声，能提供更优的图像质量，但由于其临床可用性有限，限制了大规模研究和临床部署。为弥合这一差距，我们提出SUMI方法——一种模拟退化-增强方法，通过利用高质量PCCT作为参考，学习逆转低质量EICT中的实际采集伪影。我们的核心见解在于显式建模现实采集退化过程，将PCCT转化为临床可行的低质量对应图像，并学习逆转这一过程。模拟退化经放射科认证医师验证具有临床真实感，从而无需大规模配对采集即可实现可靠监督。基于此项技术贡献，我们实现了以下成果：（1）在1,046例PCCT上训练潜在扩散模型，首先使用来自145家医院的这些PCCT和405,379例EICT预训练自编码器，提取通用CT潜在特征并发布以供其他生成式医学成像任务复用；（2）构建了包含超过17,316例公开EICT的大规模数据集，将其增强至PCCT级质量，并配有经放射科医师验证的体素级气道树、动脉、静脉、肺叶和肺段标注；（3）展现了显著改进：在外部数据上，SUMI在SSIM和PSNR上分别超越最先进图像翻译方法15%和20%，在读者研究中提升放射科医师评定的临床实用性，并增强下游顶级病灶检测性能，灵敏度最高提升15%，F1分数最高提升10%。我们的结果表明，新兴成像进展可通过有限的高质量扫描作为参考，系统性地蒸馏至常规EICT中。