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Related Concept Videos

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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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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A Joint Acquisition-Estimation Framework for MR Phase Imaging.

Joseph Dagher

    Information Processing in Medical Imaging : Proceedings of the ... Conference
    |July 30, 2015
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    Summary
    This summary is machine-generated.

    This study introduces a new framework for Magnetic Resonance (MR) phase imaging, overcoming challenges like phase aliasing and noise. The method improves MR phase imaging reliability and performance significantly.

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    Area of Science:

    • Medical Imaging
    • Signal Processing
    • Biophysics

    Background:

    • Magnetic Resonance (MR) phase imaging faces challenges including phase aliasing, noise, and unknown coil array offsets.
    • Existing acquisition, reconstruction, and estimation methods inadequately address these challenges, limiting reliable phase domain information processing.

    Purpose of the Study:

    • To propose a joint acquisition-processing framework for Magnetic Resonance (MR) phase imaging that rigorously addresses fundamental challenges.
    • To enhance the reliability and performance of MR phase imaging through a novel theoretical approach.

    Main Methods:

    • Developed a joint acquisition-processing framework for MR phase imaging.
    • Acquired multi-coil complex data without increasing acquisition time.
    • Applied an optimal voxel-per-voxel estimation algorithm.

    Main Results:

    • The proposed framework effectively addresses phase aliasing, noise, and coil offsets in MR phase imaging.
    • Achieved performance gains up to an order of magnitude compared to existing methods.
    • Demonstrated enhanced reliability in MR phase imaging information processing.

    Conclusions:

    • The joint acquisition-processing framework offers a significant advancement in MR phase imaging.
    • The method provides a robust solution for overcoming inherent challenges in MR phase data.
    • This approach improves the accuracy and utility of phase-based MR information.