In the various papers published in the field of super-resolution microscopy, denoising of raw images based on block-matching and 3D filtering (BM3D) was rarely reported. BM3D for blocks of different sizes was studied. The denoising ability is related to block sizes. The larger the block is, the better the denoising effect is. When the block size is >40, a good denoising effect can be achieved. Denoising has a great influence on the super-resolution reconstruction effect and the reconstruction time. Better super-resolution reconstruction and shorter reconstruction time can be achieved after denoising. Using compressed sensing, only 20 raw images are needed for super-resolution reconstruction. The temporal resolution is less than half a second. The spatial resolution is also greatly improved.
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http://dx.doi.org/10.1093/jmicro/dfac029 | DOI Listing |
Korean J Radiol
January 2025
Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Objective: The aim of this study was to compare image quality features and lesion characteristics between a faster deep learning (DL) reconstructed T2-weighted (T2-w) fast spin-echo (FSE) Dixon sequence with super-resolution (T2) and a conventional T2-w FSE Dixon sequence (T2) for breast magnetic resonance imaging (MRI).
Materials And Methods: This prospective study was conducted between November 2022 and April 2023 using a 3T scanner. Both T2 and T2 sequences were acquired for each patient.
Sci Rep
January 2025
Divisions of Physical Therapy and Rehabilitation Science, Department of Family Medicine and Community Health, University of Minnesota, Minneapolis, MN, 55455, USA.
OrthoFusion, an intuitive super-resolution algorithm, is presented in this study to enhance the spatial resolution of clinical CT volumes. The efficacy of OrthoFusion is evaluated, relative to high-resolution CT volumes (ground truth), by assessing image volume and derived bone morphological similarity, as well as its performance in specific applications in 2D-3D registration tasks. Results demonstrate that OrthoFusion significantly reduced segmentation time, while improving structural similarity of bone images and relative accuracy of derived bone model geometries.
View Article and Find Full Text PDFAJNR Am J Neuroradiol
January 2025
Department of Radiology (M.Z., N.W., S.H., X.L., H.Z., C.Y., Q.S.), The First Affiliated Hospital of Dalian Medical University, Dalian, China
Background And Purpose: DWI is crucial for detecting infarction stroke. However, its spatial resolution is often limited, hindering accurate lesion visualization. Our aim was to evaluate the image quality and diagnostic confidence of deep learning (DL)-based super-resolution reconstruction for brain DWI of infarction stroke.
View Article and Find Full Text PDFCell Commun Signal
January 2025
Department of Immunology, University of Connecticut School of Medicine, Connecticut, Farmington, 06030, USA.
Background: Neutrophils are the most abundant leukocytes in human blood, and their recruitment is essential for innate immunity and inflammatory responses. The initial and critical step of neutrophil recruitment is their adhesion to vascular endothelium, which depends on G protein-coupled receptor (GPCR) triggered integrin inside-out signaling that induces β2 integrin activation and clustering on neutrophils. Kindlin-3 and talin-1 are essential regulators for the inside-out signaling induced β2 integrin activation.
View Article and Find Full Text PDFLife (Basel)
December 2024
School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA.
Amyloid PET imaging plays a crucial role in the diagnosis and research of Alzheimer's disease (AD), allowing non-invasive detection of amyloid-β plaques in the brain. However, the low spatial resolution of PET scans limits the accurate quantification of amyloid deposition due to partial volume effects (PVE). In this study, we propose a novel approach to addressing PVE using a latent diffusion model for resolution recovery (LDM-RR) of PET imaging.
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