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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 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 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 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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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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A comprehensive survey on deep learning techniques in CT image quality improvement.

Disen Li1, Limin Ma1, Jining Li1

  • 1College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110819, China.

Medical & Biological Engineering & Computing
|August 13, 2022
PubMed
Summary

Deep learning enhances computed tomography (CT) image quality by utilizing vast imaging data. This review explores deep learning algorithms for improving CT scans, particularly in postprocessing, and suggests future research avenues.

Keywords:
Deep learningImage denoisingMetal artifact correctionSuper-resolution imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • High-quality computed tomography (CT) images are crucial for accurate clinical diagnosis.
  • Current CT image quality is often limited by reconstruction algorithms and other factors, necessitating improvement.
  • Traditional CT techniques do not fully leverage the extensive imaging data accumulated during scans.

Purpose of the Study:

  • To survey deep learning algorithms developed for improving CT image quality.
  • To highlight the potential of deep learning in utilizing accumulated CT data for enhanced image reconstruction and postprocessing.
  • To identify future research directions in the application of deep learning for CT image enhancement.

Main Methods:

  • Review of existing literature on deep learning algorithms applied to CT image quality improvement.
  • Categorization and analysis of deep learning approaches, with a focus on postprocessing techniques.
  • Identification of patterns and capabilities of deep learning in learning from hierarchical data structures.

Main Results:

  • Deep learning offers novel approaches to CT image quality enhancement by effectively utilizing large datasets.
  • Numerous deep learning algorithms have been proposed, particularly for postprocessing applications.
  • The hierarchical structure of deep learning enables learning complex patterns from CT imaging data.

Conclusions:

  • Deep learning presents a promising avenue for overcoming limitations in current CT image quality.
  • Further research into deep learning algorithms can significantly advance CT imaging for clinical diagnosis.
  • Exploiting the full potential of accumulated CT data through deep learning is key for future advancements.