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Related Concept Videos

X-ray Imaging01:24

X-ray Imaging

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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Phase-Contrast Microscopes
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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Difference from Background: Limit of Detection01:05

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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Related Experiment Video

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Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
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Content-adaptive ghost imaging of dynamic scenes.

Ziwei Li, Jinli Suo, Xuemei Hu

    Optics Express
    |May 4, 2016
    PubMed
    Summary
    This summary is machine-generated.

    Computational ghost imaging can now capture dynamic scenes using a novel content-adaptive approach. This method leverages image redundancy for high-quality reconstruction with fewer measurements, enabling real-time imaging.

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    Area of Science:

    • Computational imaging
    • Optical physics
    • Image processing

    Background:

    • Traditional 2D ghost imaging is limited by long acquisition times, preventing its use in dynamic scenes.
    • Nature images exhibit spatial-temporal redundancy, which is dependent on the scene's content.
    • This redundancy offers a potential avenue for improving ghost imaging efficiency.

    Purpose of the Study:

    • To develop a content-adaptive computational ghost imaging approach for dynamic scenes.
    • To achieve high reconstruction quality with a reduced number of measurements.
    • To enable ghost imaging of dynamic scenes by exploiting inter-frame redundancy.

    Main Methods:

    • Proposed a content-adaptive computational ghost imaging algorithm.
    • Utilized iterative reweighted optimization to leverage content-adaptive inter-frame redundancy.
    • Computed non-uniform weights from temporal-correlated frame sequences.

    Main Results:

    • Achieved high reconstruction quality with a small number of measurements.
    • Successfully demonstrated ghost imaging of dynamic scenes.
    • Enabled dynamic imaging at 16 frames per second (fps) with 64x64-pixel resolution.

    Conclusions:

    • The proposed content-adaptive approach overcomes the limitations of traditional ghost imaging for dynamic scenes.
    • Iterative reweighted optimization effectively utilizes temporal redundancy for improved performance.
    • This method opens possibilities for real-time ghost imaging applications.