Non-ideal measurement computed tomography (NICT), which sacrifices optimal imaging standards for new advantages in CT imaging, is expanding the clinical application scope of CT images. However, with the reduction of imaging standards, the image quality has also been reduced, extremely limiting the clinical acceptability. Although numerous studies have demonstrated the feasibility of deep learning for the NICT enhancement in specific scenarios, their high data cost and limited generalizability have become large obstacles. The recent research on the foundation model has brought new opportunities for building a universal NICT enhancement model - bridging the image quality degradation with minimal data cost. However, owing to the challenges in the collection of large pre-training datasets and the compatibility of data variation, no success has been reported. In this paper, we propose a multi-scale integrated Transformer AMPlifier (TAMP), the first imaging foundation model for universal NICT enhancement. It has been pre-trained on a large-scale physical-driven simulation dataset with 3.6 million NICT-ICT image pairs, and is able to directly generalize to the NICT enhancement tasks with various non-ideal settings and body regions. Via the adaptation with few data, it can further achieve professional performance in real-world specific scenarios. Our extensive experiments have demonstrated that the proposed TAMP has significant potential for promoting the exploration and application of NICT and serving a wider range of medical scenarios.

翻译：非理想测量计算机断层扫描（NICT）通过牺牲部分最优成像标准，为CT成像带来了新的优势，正在扩展CT图像的临床应用范围。然而，随着成像标准的降低，图像质量也随之下降，极大地限制了其临床可接受度。尽管大量研究已证明深度学习在特定场景下增强NICT的可行性，但其高昂的数据成本和有限的泛化能力已成为主要障碍。近期关于基础模型的研究为构建通用的NICT增强模型——以最小的数据成本弥合图像质量退化——带来了新的机遇。然而，由于大规模预训练数据集收集的挑战以及数据变异兼容性的问题，目前尚未有成功的报道。本文提出了一种多尺度集成Transformer放大器（TAMP），这是首个用于通用NICT增强的成像基础模型。该模型已在包含360万个NICT-ICT图像对的大规模物理驱动仿真数据集上进行了预训练，能够直接泛化至具有各种非理想设置和身体区域的NICT增强任务。通过少量数据的适配，它可以在真实世界的特定场景中进一步达到专业性能。我们的大量实验表明，所提出的TAMP在促进NICT的探索与应用、服务更广泛的医疗场景方面具有巨大潜力。