Imaging foundation model for universal enhancement of non-ideal measurement CT
This addresses the limitation of deep learning methods for CT image enhancement by providing a more generalizable solution for clinical applications, though it appears incremental as it builds on existing transformer and foundation model concepts.
The paper tackled the problem of degraded image quality in non-ideal measurement CT (NICT) due to suboptimal protocols, proposing TAMP, a foundation model that enhances NICT images, achieving improved quality and clinical acceptability across diverse settings.
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 quality and clinical acceptability, underscoring its significant potential to advance CT imaging and broaden NICT applications in clinical practice.