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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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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.
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Computed Tomography (CT) scan:
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X-ray Imaging01:24

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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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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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Radiological Investigation I: X-ray and CT01:30

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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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Unified Supervised-Unsupervised (SUPER) Learning for X-Ray CT Image Reconstruction.

Siqi Ye, Zhipeng Li, Michael T McCann

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    We introduce a unified framework for X-ray computed tomography (CT) image reconstruction, combining supervised and unsupervised learning. This SUPER learning approach enhances low-dose CT imaging by integrating deep network priors with other models for superior results.

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    Area of Science:

    • Medical Imaging
    • Computational Imaging
    • Machine Learning

    Background:

    • Traditional model-based image reconstruction (MBIR) uses basic priors.
    • Machine learning offers supervised and unsupervised approaches with limitations.
    • X-ray computed tomography (CT) reconstruction faces challenges, especially at low doses.

    Purpose of the Study:

    • To propose a unified supervised-unsupervised (SUPER) learning framework for X-ray CT image reconstruction.
    • To integrate deep network priors with unsupervised or analytical priors within MBIR.
    • To improve low-dose CT image reconstruction quality.

    Main Methods:

    • Developed a unified SUPER learning framework for X-ray CT.
    • Utilized a fixed-point iteration analysis to combine priors within MBIR.
    • Employed a bilevel optimization scheme, alternating network weight updates and reconstruction updates.
    • Trained and tested on the NIH AAPM Mayo Clinic Low Dose CT dataset.

    Main Results:

    • SUPER models demonstrated superior performance over standalone supervised methods and iterative MBIR.
    • Evaluated various combinations of supervised, unsupervised, and analytical priors.
    • Achieved rapid convergence in practice for the proposed training algorithm.
    • Numerical and visual results confirmed the efficacy of the unified SUPER approach.

    Conclusions:

    • The unified SUPER learning framework effectively enhances X-ray CT image reconstruction, particularly for low-dose applications.
    • Integrating diverse prior types within a single framework offers significant advantages over traditional and standalone ML methods.
    • The proposed method provides a robust and efficient solution for improving CT image quality.