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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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X-ray Imaging01:24

X-ray Imaging

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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

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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Electron Microscope Tomography and Single-particle Reconstruction01:07

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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
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Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

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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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Positron Emission Tomography01:29

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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
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Updated: Oct 26, 2025

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
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Deep Interactive Denoiser (DID) for X-Ray Computed Tomography.

Ti Bai, Biling Wang, Dan Nguyen

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    Summary
    This summary is machine-generated.

    Deep interactive denoiser (DID) offers a flexible solution for low-dose computed tomography (LDCT) imaging. This method generates multiple image options with varying noise-resolution trade-offs, enhancing clinical task suitability and model generalizability.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Image Processing

    Background:

    • Low-dose computed tomography (LDCT) is crucial for diagnostics and interventions.
    • Deep learning (DL) denoisers enhance LDCT quality but face challenges with fixed outputs and generalizability.
    • Existing DL denoisers struggle to provide varied noise-resolution trade-offs for diverse clinical needs.

    Purpose of the Study:

    • To introduce a flexible method for generating multiple image candidates with adjustable noise-resolution trade-offs from LDCT.
    • To enhance the adaptability and generalizability of deep learning denoisers for various clinical applications.
    • To enable real-time interactive selection of optimal denoised images for specific diagnostic tasks.

    Main Methods:

    • A lightweight optimization process, termed Deep Interactive Denoiser (DID), was developed.
    • DID operates during the testing phase, enhancing existing DL-based denoisers.
    • The method generates multiple image candidates with different noise-resolution trade-offs in real time.

    Main Results:

    • DID successfully produced multiple image candidates with varying noise-resolution trade-offs.
    • The method demonstrated excellent generalizability across different network architectures.
    • DID maintained performance across diverse training and testing datasets with varying noise levels.

    Conclusions:

    • Deep Interactive Denoiser (DID) addresses key limitations of current DL-based denoisers for LDCT.
    • The approach provides interactive control over image quality, improving clinical utility.
    • DID enhances the adaptability and robustness of LDCT denoising techniques.