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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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Imaging Studies III: Computed Tomography01:27

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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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Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

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The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
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Block Diagram Reduction01:22

Block Diagram Reduction

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The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
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Elements of Block Diagrams01:25

Elements of Block Diagrams

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Block diagrams serve as a visual representation of the input-output relationships within a system. An illustrative example is a heating system, where the set temperature activates the furnace to warm the room to the desired level. Block diagrams are versatile, modeling linear systems through Laplace transform variables and nonlinear systems using time domain variables.
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Dynamic Equilibrium02:20

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A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
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High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals
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U-net-based blocked artifacts removal method for dynamic computed tomography.

Bo Wang, Zhiqiang Chen, Wim Dewulf

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    This study introduces deep learning methods to remove artifacts in dynamic computed tomography (CT) scans of airplane engines. These techniques improve in situ nondestructive testing by enhancing the clarity of rotating engine parts.

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    Area of Science:

    • Engineering
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Airplane engines require regular inspections for safe operation, making nondestructive testing crucial.
    • Dynamic computed tomography (CT) systems utilizing rotor self-rotation offer in situ testing capabilities.
    • Static engine components cause artifacts in CT reconstructions, hindering accurate condition assessment.

    Purpose of the Study:

    • To develop and evaluate deep learning-based methods for removing static part artifacts in dynamic CT.
    • To improve the quality of reconstructed images for in situ airplane engine inspections.
    • To enhance the diagnostic accuracy of dynamic CT for rotating machinery.

    Main Methods:

    • Proposed two deep learning approaches utilizing the U-Net architecture for artifact correction.
    • Implemented correction in the projection domain by estimating and subtracting static part projections.
    • Reconstructed the engine rotor from corrected projections obtained via dynamic CT.

    Main Results:

    • Successfully removed blocked artifacts caused by static engine parts in CT reconstructions.
    • Demonstrated effective recovery of fine details in the rotating parts of the engine.
    • Validated the practical applicability and effectiveness of the proposed deep learning methods.

    Conclusions:

    • The developed deep learning methods are effective for artifact removal in dynamic CT of airplane engines.
    • These techniques significantly improve the utilization of dynamic CT for in situ engine testing.
    • Enhanced image quality facilitates more accurate assessment of engine health and component integrity.