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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

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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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Imaging Studies II: Ultrasonography01:24

Imaging Studies II: Ultrasonography

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IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
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Shearing Stress01:19

Shearing Stress

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Shearing stress, denoted by the Greek letter tau (τ), is stress caused by forces acting transversely on an object. These forces create internal ones within the entity in the plane where the external forces are applied. The resultant of these internal forces is the shear in the section.
The average shearing stress can be calculated by dividing the shear by the area of the cross-section.
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Shearing Strain01:20

Shearing Strain

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The shearing strain represents a cubic element's angular change when subjected to shearing stress. This type of stress can transform a cube into an oblique parallelepiped without influencing normal strains. The cubic element experiences a significant transformation when exposed solely to shearing stress. Its shape alters from a perfect cube into a rhomboid, clearly demonstrating the effect of shearing strain. The degree of this strain is considered positive if it reduces the angle between the...
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Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
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Related Experiment Video

Updated: Dec 22, 2025

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography
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Shearlet Enhanced Snapshot Compressive Imaging.

Peihao Yang, Linghe Kong, Xiao-Yang Liu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |May 10, 2020
    PubMed
    Summary
    This summary is machine-generated.

    A new Shearlet enhanced Snapshot Compressive Imaging (SeSCI) algorithm reconstructs high-dimensional data from single measurements. SeSCI significantly improves speed and fidelity over existing methods.

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    Area of Science:

    • Optics and Imaging
    • Signal Processing
    • Computational Imaging

    Background:

    • Snapshot compressive imaging (SCI) captures high-dimensional data using low-dimensional sensors.
    • Existing SCI reconstruction algorithms face limitations in speed and fidelity.
    • SCI encodes multiple frames into a single measurement, requiring sophisticated reconstruction.

    Purpose of the Study:

    • To develop a novel and efficient reconstruction algorithm for snapshot compressive imaging.
    • To enhance the speed and fidelity of image reconstruction in SCI.
    • To leverage sparse representations in both frequency and shearlet domains for improved SCI.

    Main Methods:

    • Proposed a Shearlet enhanced Snapshot Compressive Imaging (SeSCI) algorithm.
    • Derived the SeSCI algorithm using the alternating direction method of multipliers (ADMM) framework.
    • Developed an efficient solution for the SeSCI algorithm and proved its convergence.

    Main Results:

    • SeSCI algorithm demonstrates significant advantages over conventional SCI reconstruction methods.
    • Achieved over 2dB improvement in Peak Signal-to-Noise Ratio (PSNR) compared to existing algorithms.
    • Realized a speed-up of more than 100× compared to state-of-the-art algorithms.

    Conclusions:

    • The SeSCI algorithm offers superior performance in terms of both speed and fidelity for snapshot compressive imaging.
    • Exploiting shearlet domain sparsity enhances the reconstruction of high-dimensional data from single measurements.
    • SeSCI represents a significant advancement for practical applications of snapshot compressive imaging.