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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 for Cardiovascular System V: CT01:28

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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 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.
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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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Related Experiment Video

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Statistical Image Restoration for Low-Dose CT using Convolutional Neural Networks.

Kihwan Choi, Sungwon Kim

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary

    This study introduces a statistical deep learning method (StatCNN) to denoise low-dose CT (LDCT) images. The approach effectively reduces noise and restores details by incorporating noise statistics, improving image quality without artifacts.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Low-dose CT (LDCT) imaging is crucial for reducing radiation exposure.
    • Noise in LDCT images can obscure diagnostic details.
    • Existing deep learning denoising methods often neglect the statistical properties of CT images.

    Purpose of the Study:

    • To develop a statistical deep learning approach for denoising LDCT images.
    • To improve image restoration by incorporating noise statistics from the sinogram domain.
    • To enhance the capability of deep learning models in preserving anatomical information in denoised LDCT images.

    Main Methods:

    • Proposed a statistical convolutional neural network (StatCNN) incorporating a novel loss function.
    • Integrated noise statistics from the sinogram domain into the image domain loss function.
    • Enhanced network receptive fields and utilized z-directional correlation with multiple CT slices for spatially-varying statistics.

    Main Results:

    • StatCNN successfully reduced noise levels in LDCT images.
    • Restored image details were preserved without introducing artifacts.
    • The method demonstrated effective transfer of image style from normal-dose CT (NDCT) to LDCT images.

    Conclusions:

    • Statistical deep learning offers a promising approach for LDCT image denoising.
    • Incorporating noise statistics improves the fidelity of image restoration.
    • The proposed StatCNN method preserves anatomical information while enhancing image quality.