Imaging foundation model for universal enhancement of non-ideal measurement CT.

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.