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

Xuanqin Mou; Junfeng Wu; Ti Bai; Qiong Xu; Hengyong Yu; Ge Wang

doi:10.1117/12.2065459

11 September 2014 Dictionary learning based low-dose x-ray CT reconstruction using a balancing principle

Xuanqin Mou, Junfeng Wu, Ti Bai, Qiong Xu, Hengyong Yu, Ge Wang

Proceedings Volume 9212, Developments in X-Ray Tomography IX; 921207 (2014) https://doi.org/10.1117/12.2065459
Event: SPIE Optical Engineering + Applications, 2014, San Diego, California, United States

Abstract

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 “balancing 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.

Citation Download Citation

Xuanqin Mou, Junfeng Wu, Ti Bai, Qiong Xu, Hengyong Yu, and Ge Wang "Dictionary learning based low-dose x-ray CT reconstruction using a balancing principle", Proc. SPIE 9212, Developments in X-Ray Tomography IX, 921207 (11 September 2014); https://doi.org/10.1117/12.2065459

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