Cuscuta chinensis is a commonly used traditional Chinese herbal medicine. Cuscuta chinensis has a long history of clinical application in the treatment of varieties of ocular diseases. This review summarized the literatures related to its clinical applications, research progresses in the ophthalmic pharmacology and active ingredients. It was aimed to provide a theoretical basis for the further development and utilization of Cuscuta Chinensis as an effective medication.

Dual-energy computed tomography (CT) reconstruction imaging technology is an important development direction in the field of CT imaging. The mainstream model of dual-energy CT reconstruction algorithm is the basis material decomposition model, and the projection decomposition is the crucial technique. The projection decomposition algorithm based on projection matching was a general method. With establishing the energy spectrum lookup table, we can obtain the stable solution by the least squares matching method. But the computation cost will increase dramatically when size of lookup table enlarges and it will slow down the computer. In this paper, an acceleration algorithm based on projection matching is proposed. The proposed algorithm makes use of linear equations and plane equations to fit the lookup table data, so that the projection value of the decomposition coefficients can be calculated quickly. As the result of simulation experiment, the acceleration algorithm can greatly shorten the running time of the program to get the stable and correct solution.

The construction of brain functional network based on resting-state functional magnetic resonance imaging (fMRI) is an effective method to reveal the mechanism of human brain operation, but the common brain functional network generally contains a lot of noise, which leads to wrong analysis results. In this paper, the least absolute shrinkage and selection operator (LASSO) model in compressed sensing is used to reconstruct the brain functional network. This model uses the sparsity of
Algorithms ; Brain/diagnostic imaging* ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging