Lei Li
8 Papers
20 Citations
Lei Li is an academic researcher. The author has contributed to research in topics: Iterative reconstruction & Reconstruction algorithm. The author has an hindex of 4, co-authored 8 publications.
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Papers
Distributed reconstruction via alternating direction method.
TL;DR: A distributed reconstruction algorithm based on TV minimization has been developed that can accelerate the alternating direction total variation minimization (ADTVM) algorithm without losing accuracy.
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3D Alternating Direction TV-Based Cone-Beam CT Reconstruction with Efficient GPU Implementation
TL;DR: An algorithm based on alternating direction total variation using local linearization and proximity technique is proposed for CBCT reconstruction and shows an excellent acceleration ratio of more than 100 compared with CPU computation without losing numerical accuracy.
NUFFT-Based Iterative Image Reconstruction via Alternating Direction Total Variation Minimization for Sparse-View CT.
TL;DR: A novel Fourier-based iterative reconstruction technique that utilizes nonuniform fast Fourier transform is presented in this study along with the advanced total variation (TV) regularization for sparse-view CT and shows excellent efficiency and rapid convergence property.
Sparse-view image reconstruction with nonlocal total variation
Hanming Zhang,Bin Yan,Linyuan Wang,Lei Li,Xi Xiaoqi,Guoen Hu +5 more
- 01 Nov 2014
TL;DR: Experimental results indicate that the proposed method could solve the above mentioned effects and reconstruct more accurate than the popular split Bregman-TV algorithm when applied to a sparse-view problem.
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Patent
CT projection denoising reconstruction method and device based on noise generation mechanism and data driving tight frame
Yan Bin,Jie Li,Ziheng Li,Lei Li,Ailong Cai,Wang Linyuan,Chao Tang,Ningning Liang,Sun Yanmin +8 more
- 16 Aug 2019
TL;DR: In this paper, a CT projection denoising reconstruction method and device based on a noise generation mechanism and a data driving tight frame is proposed, and the method comprises the steps: firstly describing the noise statistical characteristics of projection data, and mining sparse prior information of the projection data; establishing a maximum posterior probability model by combining the two data; carrying out iterative solution on the model to obtain denoised sine map data; and acquiring CT image by reconstruction.
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