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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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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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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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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
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Preparation and Observation of Thick Biological Samples by Scanning Transmission Electron Tomography
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Computed tomography imaging spectrometry based on superiorization and guided image filtering.

Weizhe Han, Qianlong Wang, Weiwei Cai

    Optics Letters
    |April 30, 2021
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    Computed tomography imaging spectrometry (CTIS) reconstructions can be improved. A new algorithm combining superiorization and guided image filtering significantly reduces artifacts and enhances precision in hyperspectral data cubes.

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    Area of Science:

    • Optics and Photonics
    • Image Reconstruction
    • Spectroscopy

    Background:

    • Computed tomography imaging spectrometry (CTIS) is a snapshot hyperspectral imaging technique.
    • CTIS acquires a 3D (${2D +}\lambda$) data cube in a single exposure.
    • CTIS reconstructions often exhibit severe artifacts due to limited projections.

    Purpose of the Study:

    • To develop an advanced iterative algorithm for CTIS reconstruction.
    • To address and mitigate artifacts in hyperspectral data cubes.
    • To improve the precision of CTIS reconstructions.

    Main Methods:

    • An iterative algorithm combining superiorization and guided image filtering was developed.
    • The algorithm leverages intrinsic hyperspectral data cube properties.
    • It also utilizes characteristics of zero-order diffraction for artifact suppression.

    Main Results:

    • Simulative studies and experiments validated the algorithm's effectiveness.
    • The proposed method demonstrated superior artifact suppression compared to the expectation maximization algorithm.
    • Enhanced precision in reconstructed hyperspectral data was achieved.

    Conclusions:

    • The novel iterative algorithm effectively overcomes CTIS reconstruction limitations.
    • This technique offers a significant advancement for hyperspectral imaging applications.
    • It provides a more precise and artifact-free method for analyzing spectral data.