首个通用CT图像增强基础模型，可提升低质量扫描的清晰度与临床可用性。
Imaging foundation model for universal enhancement of non-ideal measurement CT
- 基于1080万组模拟CT数据训练，采用多尺度Transformer架构实现跨场景泛化
- 在多种扫描条件和身体部位上均显著提升图像质量，医生评估更认可
- 仅需少量真实数据微调即可适配特定临床需求，适合医疗影像快速部署
非理想测量计算机断层扫描（NICT）通过次优成像协议拓展了CT应用范围，但由此带来的图像质量下降限制了其临床接受度。尽管已有深度学习方法用于增强NICT图像，但其对大规模训练数据的依赖及跨场景泛化能力有限，制约了实际应用。本文提出首个用于通用NICT增强的成像基础模型——多尺度集成Transformer AMPlifier（TAMP）。该模型在1080万组物理驱动的模拟NICT图像对上预训练，可在不同NICT设置、缺陷程度及身体区域间有效泛化。此外，一种参数高效的微调策略使其仅需少量扫描切片即可适应具体临床场景。大量实验（包括放射科医生评估与真实世界验证）表明，TAMP持续提升图像质量与临床可接受性，展现出显著潜力，有望推动CT成像发展并扩大NICT在临床中的应用。
原文摘要 · Abstract (English)
Non-ideal measurement computed tomography (NICT) employs suboptimal imaging protocols to expand CT applications. However, the resulting trade-offs degrade image quality, limiting clinical acceptability. Although deep learning methods have been used to enhance NICT images, their reliance on large training datasets and limited generalizability across diverse settings hinder practical use. We propose the multi-scale integrated Transformer AMPlifier (TAMP), the first imaging foundation model for universal NICT enhancement. Pre-trained on 10.8 million physics-driven simulated NICT images, TAMP generalizes effectively across various NICT settings, defect degrees, and body regions. Moreover, a parameter-efficient fine-tuning strategy enables TAMP to adapt to specific clinical scenarios using only few slices. Extensive experiments, including radiologists and real-world validations, demonstrate that TAMP consistently improves image quality and clinical acceptability, underscoring its significant potential to advance CT imaging and broaden NICT applications in clinical practice.
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