Zhanli Hu1,2,3, Qiegen Liu4, Na Zhang1,5, Yunwan Zhang1, Xi Peng1,3, Peter Z Wu1, Hairong Zheng1,3, Dong Liang1,3. 1. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. 2. Department of Biomedical Engineering, University of California, Davis, CA, USA. 3. Beijing Center for Mathematics and Information Interdisciplinary Sciences, Beijing, China. 4. Department of Electronic Information Engineering, Nanchang University, Nanchang, China. 5. Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Abstract
BACKGROUND: Decreasing the number of projections is an effective way to reduce the radiation dose exposed to patients in medical computed tomography (CT) imaging. However, incomplete projection data for CT reconstruction will result in artifacts and distortions. OBJECTIVE: In this paper, a novel dictionary learning algorithm operating in the gradient-domain (Grad-DL) is proposed for few-view CT reconstruction. Specifically, the dictionaries are trained from the horizontal and vertical gradient images, respectively and the desired image is reconstructed subsequently from the sparse representations of both gradients by solving the least-square method. METHODS: Since the gradient images are sparser than the image itself, the proposed approach could lead to sparser representations than conventional DL methods in the image-domain, and thus a better reconstruction quality is achieved. RESULTS: To evaluate the proposed Grad-DL algorithm, both qualitative and quantitative studies were employed through computer simulations as well as real data experiments on fan-beam and cone-beam geometry. CONCLUSIONS: The results show that the proposed algorithm can yield better images than the existing algorithms.
BACKGROUND: Decreasing the number of projections is an effective way to reduce the radiation dose exposed to patients in medical computed tomography (CT) imaging. However, incomplete projection data for CT reconstruction will result in artifacts and distortions. OBJECTIVE: In this paper, a novel dictionary learning algorithm operating in the gradient-domain (Grad-DL) is proposed for few-view CT reconstruction. Specifically, the dictionaries are trained from the horizontal and vertical gradient images, respectively and the desired image is reconstructed subsequently from the sparse representations of both gradients by solving the least-square method. METHODS: Since the gradient images are sparser than the image itself, the proposed approach could lead to sparser representations than conventional DL methods in the image-domain, and thus a better reconstruction quality is achieved. RESULTS: To evaluate the proposed Grad-DL algorithm, both qualitative and quantitative studies were employed through computer simulations as well as real data experiments on fan-beam and cone-beam geometry. CONCLUSIONS: The results show that the proposed algorithm can yield better images than the existing algorithms.