Dictionary learning based low-dose x-ray CT reconstruction using a balancing principle

The high utility and wide applicability of x-ray imaging has led to a rapidly increased number of CT scans over the past years, and at the same time an elevated public concern on the potential risk of x-ray radiation to patients. Hence, a hot topic is how to minimize x-ray dose while maintaining the image quality. The low-dose CT strategies include modulation of x-ray flux and minimization of dataset size. However, these methods will produce noisy and insufficient projection data, which represents a great challenge to image reconstruction. Our team has been working to combine statistical iterative methods and advanced image processing techniques, especially dictionary learning, and have produced excellent preliminary results. In this paper, we report recent progress in dictionary learning based low-dose CT reconstruction, and discuss the selection of regularization parameters that are crucial for the algorithmic optimization. The key idea is to use a "balance principle" based on a model function to choose the regularization parameters during the iterative process, and to determine a weight factor empirically for address the noise level in the projection domain. Numerical and experimental results demonstrate the merits of our proposed reconstruction approach.