In image-guided surgeries (IGSs) and radiology, images are the main source of information. As image data provide the differentiation between normal and abnormal tissues in the human, the images need to be reliable and they need to provide accurate spatial representation of the patient. This research concentrates on the accuracy assessment of IGS devices in general and then specifically on the spatial accuracy of a common magnetic resonance (MR) imager and a mobile three-dimensional surgical computed tomography (CT) scanner. The accuracy assessment tool had been designed to be universal and to enable its use in the hospital setting. In this study, it was used in detecting the spatial accuracy of a commercial surgical CT scanner, the O-arm, and a 1.5-T MR imager. The results show the tendency of magnetic resonance imaging to produce slight decreases in spatial accuracy toward the fringes of the images from the isocenter. Furthermore, the results indicate that the accuracy of both scanners was within pixel size and thus highly accurate in the region of surgical interest of this study.
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http://dx.doi.org/10.1117/1.JMI.1.1.015502 | DOI Listing |
J 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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January 2025
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, 454003, China.
Owing to China's massive area and vastly differing regional variations in the types and efficiency of energy, the spatiotemporal distributions of regional carbon emissions (CE) vary widely. Regional CE study is becoming more crucial for determining the future course of sustainable development worldwide. In this work, two types of nighttime light data were integrated to expand the study's temporal coverage.
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January 2025
Department of Forest Engineering, Faculty of Agronomy and Forest Engineering, Eduardo Mondlane University, P.O. Box 257, Maputo, Mozambique.
Seasonally dry tropical woodlands are vital for climate change mitigation, yet their full potential in carbon storage remains poorly understood. This is largely due to the lack of species-specific allometric models tailored to these ecosystems. To address this knowledge gap, this study aimed to develop species-specific biomass allometric equations (BAEs) for accurately estimating both above- and below-ground biomass of Colophospermum mopane (J.
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January 2025
Center for Advanced Laser Technologies (CETAL), National Institute for Lasers, Plasma and Radiation Physics, Magurele-Ilfov, 077125, Romania.
Nature offers unique examples that help humans produce artificial systems which mimic specific functions of living organisms and provide solutions to complex technical problems of the modern world. For example, the development of 3D micro-nanostructures that mimic nocturnal insect eyes (optimized for night vision), emerges as promising technology for detection in IR spectral region. Here, we report a proof of principle concerning the design and laser 3D printing of all ultrastructural details of nocturnal moth Grapholita Funebrana eyes, for potential use as microlens arrays for IR detection systems.
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January 2025
Department of Data Science and Artificial Intelligence, Sunway University, 47500, Petaling Jaya, Selangor Darul Ehsan, Malaysia.
Precise segmentation of retinal vasculature is crucial for the early detection, diagnosis, and treatment of vision-threatening ailments. However, this task is challenging due to limited contextual information, variations in vessel thicknesses, the complexity of vessel structures, and the potential for confusion with lesions. In this paper, we introduce a novel approach, the MSMA Net model, which overcomes these challenges by replacing traditional convolution blocks and skip connections with an improved multi-scale squeeze and excitation block (MSSE Block) and Bottleneck residual paths (B-Res paths) with spatial attention blocks (SAB).
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