Visual language model-assisted spectral CT reconstruction by diffusion and low-rank priors from limited-angle measurements.

Objective: Spectral computed tomography (CT) is a critical tool in clinical practice, offering capabilities in multi-energy spectrum imaging and material identification. The limited-angle (LA) scanning strategy has attracted attention for its advantages in fast data acquisition and reduced radiation exposure, aligning with the as low as reasonably achievable principle. However, most deep learning-based methods require separate models for each LA setting, which limits their flexibility in adapting to new conditions. In this study, we developed a novel Visual-Language model-assisted Spectral CT Reconstruction (VLSR) method to address LA artifacts and enable multi-setting adaptation within a single model.

Approach: The VLSR method integrates the image-text perception ability of visual-language models and the image generation potential of diffusion models. Prompt engineering is introduced to better represent LA artifact characteristics, further improving artifact accuracy. Additionally, a collaborative sampling framework combining data consistency, low-rank regularization, and image-domain diffusion models is developed to produce high-quality and consistent spectral CT reconstructions.

Main results: The performance of VLSR is superior to other comparison methods. Under the scanning angles of 90° and 60° for simulated data, the VLSR method improves peak signal noise ratio by at least 0.41 dB and 1.13 dB compared with other methods.

Significance: VLSR method can reconstruct high-quality spectral CT images under diverse LA configurations, allowing faster and more flexible scans with dose reductions.