[2410.01591] Imaging foundation model for universal enhancement of non-ideal measurement CT

Electrical Engineering and Systems Science > Image and Video Processing arXiv:2410.01591 (eess) [Submitted on 2 Oct 2024 (v1), last revised 22 Mar 2026 (this version, v3)] Title:Imaging foundation model for universal enhancement of non-ideal measurement CT Authors:Rongjun Ge, Yuxin Liu, Zhan Wu, Shangwen Yang, Yuan Gao, Chenyu You, Ge Wang, Shuo Li, Yuting He, Yang Chen View a PDF of the paper titled Imaging foundation model for universal enhancement of non-ideal measurement CT, by Rongjun Ge and 9 other authors View PDF HTML (experimental) Abstract: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 qual...