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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

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.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
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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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Updated: Oct 17, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Advances in deep learning for computed tomography denoising.

Sung Bin Park1

  • 1Department of Radiology, Chung-Ang University Hospital, Seoul 06973, South Korea. pksungbin@paran.com.

World Journal of Clinical Cases
|October 8, 2021
PubMed
Summary
This summary is machine-generated.

Computed tomography (CT) scans use radiation, raising health concerns. Deep learning image reconstruction effectively reduces CT radiation dose while maintaining diagnostic image quality.

Keywords:
Computer-assisted imaging processingDeep learningDenoisingIterative reconstructionRadiation dose

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Radiation Oncology

Background:

  • Computed tomography (CT) utilization is increasing, contributing significantly to medical radiation exposure.
  • Concerns regarding the harmful effects of radiation necessitate dose reduction strategies in CT procedures.
  • Lowering CT radiation dose can negatively impact image quality and diagnostic accuracy due to reduced signal-to-noise ratio.

Discussion:

  • Deep learning (DL) techniques are emerging as powerful tools for image processing and noise reduction in medical imaging.
  • DL-based image reconstruction offers a promising approach to mitigate the trade-off between radiation dose and image quality in CT.
  • Commercial availability of DL applications for diagnostic imaging signifies a shift towards AI-enhanced radiology.

Key Insights:

  • Deep learning image reconstruction can significantly improve the quality of clinical CT images.
  • This technology has the potential to reduce patient radiation burden without compromising diagnostic performance.
  • DL-based denoising effectively addresses noise and artifacts inherent in low-dose CT scans.

Outlook:

  • Further advancements in deep learning are expected to enhance CT image quality and patient safety.
  • Widespread adoption of DL reconstruction could revolutionize diagnostic imaging protocols.
  • Future research will likely focus on optimizing DL algorithms for various clinical applications and patient populations.