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.

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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.

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Automatically matching corresponding regions of interest (ROIs) on two-view images is valuable in breast cancer diagnosis, benefiting of saving time and cutting the workload. We propose a method for matching the corresponding ROIs by integrating the geometric model and image similarity searching. The geometric model is implemented by restoring a free breast in the 3D space from two-view preprocessed breast contours. Then, the possible position of the ROI center on cranio-caudal (CC)/medio-lateral oblique (MLO) view image is represented by three feature points in the 3D space. As the view changes, these points can be mapped onto the MLO/CC view image. A matching strip is created later according to the confidence interval, within which the specific position of the ROI can be located by image similarity searching. The experiments were conducted on 273 pairs of mammograms with 400 calcifications and 284 pairs with 300 masses to verify the accuracy of the geometric model and similarity searching. The mean absolute error between the curves and the ROI centers was 3.36 ± 2.90 mm. For 95% detection sensitivity, the confidence interval was ±8.77 mm. For calcifications, the mean distance between the centers of the matched ROIs and the reference was 3.92 ± 4.61 mm. About 93.46% cases had overlap greater than 50%, and 92.46% cases had overlap greater than 75%. For masses, the mean distance was 6.15 ± 7.08 mm. About 88.46% cases had overlap greater than 50%, and 85.58% cases had overlap greater than 75%. INDEX TERMS Mammogram, geometric model, matching ROIs, similarity measure.

Developing aqueous-phase and metal-free room temperature (RT) long-afterglow materials should be of great significance for printable anti-counterfeiting encryption, LED and autofluorescence-free biosensing and bioimaging. However, the utility and reliability are...