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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 I: CT and MRI01:14

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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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Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Imaging Studies II: Positron Emission Tomography and Scintigraphy01:25

Imaging Studies II: Positron Emission Tomography and Scintigraphy

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Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
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Correlation modeling for compression of computed tomography images.

Juan Munoz-Gomez, Joan Bartrina-Rapesta, Michael W Marcellin

    IEEE Journal of Biomedical and Health Informatics
    |July 24, 2014
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    Summary

    This study introduces a method to predict when multicomponent transforms improve computed tomography (CT) image coding. Multicomponent transforms are beneficial for CT images with a high correlation coefficient (r > 0.87).

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    Area of Science:

    • Medical Imaging
    • Image Processing
    • Computer Science

    Background:

    • Computed tomography (CT) generates 3-D images using X-ray exposures.
    • Current coding methods for CT images sometimes fail to outperform simpler slice-by-slice approaches.
    • Exploiting correlations among CT slices is key for efficient image coding.

    Purpose of the Study:

    • To develop a predictive analysis for the efficacy of multicomponent transforms in CT image coding.
    • To determine the correlation threshold at which multicomponent transforms become advantageous.
    • To improve the efficiency of 3-D medical image compression.

    Main Methods:

    • Modeling the correlation coefficient (r) based on image acquisition parameters.
    • Developing a novel analysis to predict the profitability of multicomponent transforms.
    • Conducting extensive experiments using data from multiple image sensors.

    Main Results:

    • The proposed analysis accurately predicts the performance of multicomponent transforms.
    • A correlation coefficient threshold of r > 0.87 was identified for the effective use of multicomponent transforms.
    • Experimental results validated the predictive model across various image sensors.

    Conclusions:

    • Multicomponent transforms offer coding gains for CT images when slice correlation is high (r > 0.87).
    • The developed analysis provides a practical tool for optimizing CT image coding strategies.
    • This research contributes to more efficient medical image compression and storage.