Side Information-Assisted Low-Dose CT Reconstruction

CT images from individual patients or different patient populations typically share similar radiological features such as textures and structures. In model-based iterative reconstruction (MBIR) for low-dose CT (LDCT) imaging, image quality enhancement can be achieved not only by relying on the intrinsic raw data, but also by incorporating side information extracted from high-quality normal-dose CT (NDCT) exemplar images. The additional side information helps overcome the inherent limitations of raw data in low-dose scanning and offers potential improvements in LDCT image quality. This study investigates the effectiveness of side information-assisted MBIR (SI-MBIR) in enhancing the quality of LDCT images. Specifically, we propose to use the noise-free exemplar images to generate side information that aligns with the structural features of regions of interest (ROIs) in the target image. Each ROI is enhanced with a custom-designed prior subspace that is derived from similar exemplar samples and reflects its unique structural and textural characteristics. We then propose an adaptive sparse modeling approach, in particular, a weighted Laplace distribution model for the prior subspace. The weighted Laplace model is carefully tuned to match the signal-to-noise ratio (SNR) of each transform band, allowing adaptive sparse modeling on different bands. Furthermore, we propose an efficient CT reconstruction algorithm based on this adaptive sparse model. Using the alternating direction method of multipliers (ADMM) framework, an optimization method for this reconstruction algorithm has been formulated. Extensive experimental studies were conducted to validate the effectiveness of the proposed algorithm. The results show that the proposed algorithm can achieve noticeable improvements over some state-of-the-art MBIR methods in terms of noise suppression and texture preservation.