Dynamical or spatial properties of charged species can be obtained using electrostatic lenses by velocity map imaging (VMI) or spatial map imaging (SMI), respectively. Here, we report an approach for extracting dynamical and spatial information from patterns in SMI images that map the initial coordinates, velocity vectors, and angular distributions of charged particles onto the detector, using the same apparatus as in VMI. Deciphering these patterns required analysis and modeling, involving both their predictions from convolved spatial and velocity distributions and fitting observed images to kinetic energies (KEs) and anisotropy parameters (βs). As the first demonstration of this capability of SMI, the ensuing photoelectrons resulting from (2 + 1) resonant ionization of water in a selected rotational state were chosen to provide a rigorous basis for comparison to VMI. Operation with low acceleration voltages led to a measured SMI pattern with a unique vertical intensity profile that could be least-squares fitted to yield KE and β, in good agreement with VMI measurement. Due to the potential for improved resolution and the extended KE range achievable by this new technique, we expect that it might augment VMI in applications that require the analysis of charged particles and particularly in processes with high KE release.
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G3 (Bethesda)
January 2025
W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Malaria Research Institute, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA.
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View Article and Find Full Text PDFJ Imaging
January 2025
School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213000, China.
Manual labeling of lesions in medical image analysis presents a significant challenge due to its labor-intensive and inefficient nature, which ultimately strains essential medical resources and impedes the advancement of computer-aided diagnosis. This paper introduces a novel medical image-segmentation framework named Efficient Generative-Adversarial U-Net (EGAUNet), designed to facilitate rapid and accurate multi-organ labeling. To enhance the model's capability to comprehend spatial information, we propose the Global Spatial-Channel Attention Mechanism (GSCA).
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January 2025
Department of Computer Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.
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