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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.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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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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Related Experiment Video

Updated: Oct 10, 2025

Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences
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Customized Total Variation Algorithm for Metal Artifact Reduction in Computed Tomography.

Ziheng Deng, Yufu Zhou, Weikang Zhang

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    |December 11, 2021
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    Summary
    This summary is machine-generated.

    Metal artifact reduction (MAR) in CT scans is improved by a new Customized Total Variation (CTV) method. This technique reduces artifacts from metal implants, offering better image quality than standard approaches.

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    Area of Science:

    • Medical Imaging
    • Image Reconstruction
    • Computational Imaging

    Background:

    • Metal artifacts pose a significant challenge in computed tomography (CT) imaging.
    • High-density metal objects disrupt CT measurements, complicating image reconstruction.
    • Compressed sensing (CS) has been used for metal artifact reduction (MAR), but can introduce secondary artifacts.

    Purpose of the Study:

    • To develop a novel method for metal artifact reduction in CT imaging.
    • To address the limitations of conventional compressed sensing algorithms in MAR.
    • To improve the accuracy and quality of CT images in the presence of metal implants.

    Main Methods:

    • A Customized Total Variation (CTV) method was developed for MAR.
    • The gradient operator in the total variation norm was redefined based on metal and tissue distribution.
    • A weighting strategy was incorporated to preserve fine image details.

    Main Results:

    • The proposed CTV method effectively reduces metal artifacts in CT images.
    • CTV demonstrated superior performance compared to conventional MAR techniques.
    • Secondary artifacts introduced by standard CS methods were mitigated.

    Conclusions:

    • The CTV method offers an improved approach to metal artifact reduction in CT.
    • This technique enhances diagnostic image quality in patients with metallic implants.
    • Customizing reconstruction algorithms based on artifact patterns is beneficial for MAR.