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.

翻译：非理想测量计算机断层扫描（NICT）采用次优成像方案以扩展CT应用。然而，由此产生的权衡会降低图像质量，限制了临床可接受性。尽管深度学习方法已被用于增强NICT图像，但其对大规模训练数据集的依赖以及在多样化场景下有限的泛化能力阻碍了实际应用。我们提出了多尺度集成Transformer放大器（TAMP），这是首个用于通用NICT增强的成像基础模型。TAMP在1080万个物理驱动的模拟NICT图像上进行预训练，能有效泛化至各种NICT设置、缺陷程度和身体区域。此外，一种参数高效的微调策略使得TAMP能够仅使用少量切片即可适应特定的临床场景。包括放射科医师评估和真实世界验证在内的广泛实验表明，TAMP能持续提升图像质量和临床可接受性，凸显了其在推进CT成像和拓宽NICT在临床实践中应用的巨大潜力。