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Abstract There are many problems in space debris monitoring with ground‐based telescopes, such as too many stars in the same field of view, uneven background and optical distortion in the optical system. We propose a two‐stage weak debris detection algorithm. In the first stage, wavelet transform is used to extract different components of three frames of images, and the median of corresponding components of the images is taken respectively to eliminate the influence of stars. In the second stage, an improved version of the faint space target extraction based on principal component analysis. The algorithm uses a smooth‐detection idea to extract target information. Based on a 150 mm aperture telescope, we improved the existing method of faint space debris extraction based on principal component analysis by introducing the smooth‐detection idea, and transformed the target detection problem into the separation problem of sparse matrix and low‐rank matrix. We applied a certain preprocessing consisting of wavelet‐based star removal and median pre‐filtering to keep as little noise and other contaminants as possible. After experimental measurements by observers, the algorithm demonstrated advanced detection capabilities on multiple indicators.

The detection of Coronavirus disease 2019 (COVID-19) is crucial for controlling the spread of the virus. Current research utilizes X-ray imaging and artificial intelligence for COVID-19 diagnosis. However, conventional X-ray scans expose patients to excessive radiation, rendering repeated examinations impractical. Ultra-low-dose X-ray imaging technology enables rapid and accurate COVID-19 detection with minimal additional radiation exposure. In this retrospective cohort study, ULTRA-X-COVID, a deep neural network specifically designed for automatic detection of COVID-19 infections using ultra-low-dose X-ray images, is presented. The study included a multinational and multicenter dataset consisting of 30,882 X-ray images obtained from approximately 16,600 patients across 51 countries. It is important to note that there was no overlap between the training and test sets. The data analysis was conducted from 1 April 2020 to 1 January 2022. To evaluate the effectiveness of the model, various metrics such as the area under the receiver operating characteristic curve, receiver operating characteristic, accuracy, specificity, and F1 score were utilized. In the test set, the model demonstrated an AUC of 0.968 (95% CI, 0.956–0.983), accuracy of 94.3%, specificity of 88.9%, and F1 score of 99.0%. Notably, the ULTRA-X-COVID model demonstrated a performance comparable to conventional X-ray doses, with a prediction time of only 0.1 s per image. These findings suggest that the ULTRA-X-COVID model can effectively identify COVID-19 cases using ultra-low-dose X-ray scans, providing a novel alternative for COVID-19 detection. Moreover, the model exhibits potential adaptability for diagnoses of various other diseases.

The COVID-19 pandemic continues to affect the world. Wuhan, the epicenter of the outbreak, underwent a 76-day lockdown. Research has indicated that the lockdown negatively impacted the quality of life of older individuals, but little is known about their specific experiences during the confinement period. Qualitative interviews were conducted with 20 elderly residents of Wuhan, aged 65 to 85, who experienced mandatory isolation throughout the pandemic. The interviews centered around three stages of experiences: the Early Lockdown stage (the first week of lockdown after the government implemented the lockdown policy in January 2020), Infection During Lockdown stage (from February to April 2020 when participants were affected by the lockdown), and the Post-Lockdown stage (after April 2020 when the government lifted the lockdown policy). We found that older adults experienced different core themes during each lockdown stage. In the Early Lockdown stage, they felt nervousness and fear while searching for information. During the Lockdown and Infection Stage, they relied on reciprocal support and adjusted to new lifestyles. In the Post-Lockdown stage, they expressed cautions, trust, and gratitude. The finding highlights the evolving emotions and coping strategies of older adults throughout the lockdown phases. This study has yielded valuable insights into the adaptations of behavior and the importance of social interactions, specifically emphasizing the significance of healthcare among the elderly population.

ObjectiveTo develop a contrast learning-based generative (CLG) model for the generation of high-quality synthetic computed tomography (sCT) from low-quality cone-beam CT (CBCT). The CLG model improves the performance of deformable image registration (DIR).MethodsThis study included 100 post-breast-conserving patients with the pCT images, CBCT images, and the target contours, which the physicians delineated. The CT images were generated from CBCT images via the proposed CLG model. We used the Sct images as the fixed images instead of the CBCT images to achieve the multi-modality image registration accurately. The deformation vector field is applied to propagate the target contour from the pCT to CBCT to realize the automatic target segmentation on CBCT images. We calculate the Dice similarity coefficient (DSC), 95 % Hausdorff distance (HD95), and average surface distance (ASD) between the prediction and reference segmentation to evaluate the proposed method.ResultsThe DSC, HD95, and ASD of the target contours with the proposed method were 0.87 ± 0.04, 4.55 ± 2.18, and 1.41 ± 0.56, respectively. Compared with the traditional method without the synthetic CT assisted (0.86 ± 0.05, 5.17 ± 2.60, and 1.55 ± 0.72), the proposed method was outperformed, especially in the soft tissue target, such as the tumor bed region.ConclusionThe CLG model proposed in this study can create the high-quality sCT from low-quality CBCT and improve the performance of DIR between the CBCT and the pCT. The target segmentation accuracy is better than using the traditional DIR.