Imaging foundation model for universal enhancement of non-ideal measurement CT.
Authors
Affiliations (12)
Affiliations (12)
- School of Instrument Science and Engineering, Southeast University, Nanjing, China.
- School of Computer Science and Engineering, Southeast University, Nanjing, China.
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
- Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China.
- Department of Electrical & Computer Engineering, Yale University, New Haven, CT, USA.
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA.
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA. [email protected].
- School of Computer Science and Engineering, Southeast University, Nanjing, China. [email protected].
- Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Nanjing, China. [email protected].
- Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, Southeast University, Nanjing, China. [email protected].
Abstract
Non-ideal measurement computed tomography (CT) 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 non-ideal measurement CT 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), an imaging foundation model for universal non-ideal measurement CT enhancement. Pre-trained on 10.8 million physics-driven simulated non-ideal measurement CT images, TAMP generalizes effectively across various non-ideal measurement CT 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 non-ideal measurement CT applications in clinical practice.