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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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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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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
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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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Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
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Cone Beam X-ray Luminescence Computed Tomography Based on Bayesian Method.

Guanglei Zhang, Fei Liu, Jie Liu

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    This study introduces a new Bayesian method to enhance cone beam X-ray luminescence computed tomography (CB-XLCT) imaging. The self-adaptive approach significantly improves image quality for biomedical research applications.

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    Area of Science:

    • Biomedical imaging
    • Medical physics
    • Radiological sciences

    Background:

    • X-ray luminescence computed tomography (XLCT) offers fused functional and anatomical imaging.
    • Existing narrow-beam XLCT has limitations in data acquisition efficiency.
    • Cone beam XLCT (CB-XLCT) promises improved efficiency but faces reconstruction challenges due to ill-posed inverse problems.

    Purpose of the Study:

    • To develop a novel method for improving image reconstruction in CB-XLCT.
    • To address the ill-conditioned nature of the inverse problem in CB-XLCT.
    • To enhance image quality and overcome limitations of previous CB-XLCT techniques.

    Main Methods:

    • Implementation of a novel Bayesian reconstruction method for CB-XLCT.
    • Utilization of a local regularization strategy based on Gaussian Markov random fields.
    • Application of an alternating optimization scheme with an iterative coordinate descent algorithm for hyperparameter calculation and image reconstruction.

    Main Results:

    • The proposed Bayesian method effectively mitigates the ill-conditioning of CB-XLCT.
    • Numerical simulations and mouse experiments demonstrated significant improvements in image quality.
    • The self-adaptive nature of the method allows for automatic hyperparameter calculation.

    Conclusions:

    • The novel Bayesian method provides a robust solution for CB-XLCT image reconstruction.
    • This approach significantly enhances image quality compared to conventional methods.
    • The developed technique holds promise for advancing molecular and functional imaging in biomedical research.