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Imaging foundation model for universal enhancement of non-ideal measurement CT
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DOI：10.1038/s41467-026-73943-y.png)
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Abstract
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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. This work presents TAMP, an imaging foundation model pre-trained on 10.8 million physics-driven simulated CT image pairs for universal enhancement of non-ideal measurement CT (NICT). TAMP adapts to diverse clinical scenarios using only a few CT slices, offering a scalable pathway to improve NICT image quality across protocols and body regions.
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