A series of new homopolymers with various degrees of polymerization derived from vinyl tetraphenylethene, that is, poly[2-(4-vinylphenyl)ethene-1,1,2-triyl)tribenzene] homopolymers, is synthesized by reversible addition-fragmentation chain transfer (RAFT) polymerization. The homopolymers exhibit a significant aggregation-induced emission (AIE) effect and an ability to assemble themselves into AIE polymer nanoparticles (NPs) during precipitation in a water/tetrahydrofuran (THF) mixture. The NPs also exhibit good dispersibility, stability, and biocompatibility. The AIE polymer NPs are used in imaging studies of HeLa cells.
The mammography is the first choice of breast cancer screening, which has proven to be the most effective screening method. An antiscatter grid is usually employed to enhance the contrast of image by absorbing unexpected scattered signals. However, the grid pattern casts shadows and grid artifacts, which severely degrade the image quality. To solve the problem, we propose the patch based frequency signal filtering for fast grid artifacts suppressing. As opposed to whole image processing synchronously, the proposed method divides image into a number of blocks for tuning filter simultaneously, which reduces the frequency interference among image blocks and saves computation time by multithread processing. Moreover, for mitigating grid artifacts more precisely, characteristic peak detection is employed in each block automatically, which can accurately identify the location of the antiscatter grid and its motion pattern. Qualitative and quantitative studies were performed on simulation and real machine data to validate the proposed method. The results show great potential for fast suppressing grid artifacts and generating high quality of digital mammography.
Inspired by the successful application of the q‐Weibull distribution in other research fields, we took the lead to use it in the field of medical devices in this work. The parameter estimation of the q‐Weibull distribution was performed using the probability plot method. The CT failure data from Nanfang Hospital in Guangzhou, China, were used to study the reliability of CT equipment at two levels: the CT system and its seven main components. In terms of evaluation accuracy, the mean squared error, Akaike's information criterion, and the determination coefficient were used to compare the accuracy of fitting of different distribution models. The results show that the accuracy of fitting the q‐Weibull distribution is higher than that of the Weibull distribution in terms of determination coefficient and mean squared error. When considering the complexity of the model, the fit accuracy of the Weibull distribution is better. The results were analyzed using reliability and failure rate plots. The q‐Weibull distribution gives a good fit for the failure data of the CT system and components. Though the Weibull distribution fits better in a few cases, the q‐Weibull distribution can describe the entire “bathtub curve” with only a set of parameters. The findings of this study can be extended to other medical devices.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.