We propose a hybrid optical-digital imaging system that can provide high-resolution retinal images without wavefront sensing or correction of the spatial and dynamic variations of eye aberrations. A methodology based on wavefront coding is implemented in a fundus camera in order to obtain a high-quality image of retinal detail. Wavefront-coded systems rely simply on the use of a cubic-phase plate in the pupil of the optical system. The phase element is intended to blur images in such a way that invariance to optical aberrations is achieved. The blur is then removed by image postprocessing. Thus, the system can provide high-resolution retinal images, avoiding all the optics needed to sense and correct ocular aberration, i.e., wavefront sensors and deformable mirrors.
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http://dx.doi.org/10.1364/OL.39.003986 | DOI Listing |
Angew Chem Int Ed Engl
January 2025
Henan University of Technology, School of Chemistry and Chemical Engineering, CHINA.
Developing of molecular crystalline materials with light-induced multiple dynamic deformation in space dimension and photochromism on time scales has attracted much attention for its potential applications in actuators, sensoring and information storage. Nevertheless, organic crystals capable of both photoinduced dynamic effects and static color change are rare, particularly for multi-component cocrystals system. In this study, we first report the construction of charge transfer co-crystals allows their light-induced solid-to-liquid transition and photochromic behaviors to be controlled by trans-stilbene (TSB) as an electron donor and 3,4,5,6-Tetrafluorophthalonitrile (TFP) as an electron acceptor.
View Article and Find Full Text PDFMed Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
Spine Deform
January 2025
Department of Spine Surgery, Eifelklinik St Brigida, St. Brigida Eifelklinik, Kammerbruchst. 8, 52152, Simmerath, Germany.
Purpose: To evaluate the sites where the tether breaks in vertebral body tethering (VBT) cases.
Methods: Intraoperative evaluation of broken tethers in patients who had anterior revision.
Inclusion Criteria: anterior revision of VBT cases with explantation of the full implant and photo documentation.
Environ Sci Pollut Res Int
January 2025
Department of Geomatics Engineering, Hacettepe University, 06800, Beytepe, Ankara, Türkiye.
This study presents a hybrid methodology for planning green spaces to enhance urban sustainability and livability, evaluating the impacts of climate change on cities. Cities, once accommodating a small population, have become major centers of migration and development since the eighteenth century. Rapid urban growth intensifies infrastructure, environmental, and social challenges.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.
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