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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

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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Use of artificial intelligence in computed tomography dose optimisation.

C H McCollough1, S Leng1

  • 1CT Clinical Innovation Center, Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, USA;

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|September 2, 2020
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances medical imaging, particularly in computed tomography (CT). AI reduces patient radiation dose and improves image quality and diagnostic accuracy in CT scans.

Keywords:
Artificial intelligenceDeep learningDose optimisationPatient doseX-ray computed tomography

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computed Tomography

Background:

  • Artificial intelligence (AI) is revolutionizing various societal sectors, including medical imaging.
  • Computed tomography (CT) benefits from AI for dose reduction and image quality enhancement.

Purpose of the Study:

  • To explore the transformative impact of AI in medical imaging, specifically within CT.
  • To highlight AI's role in optimizing radiation dose, image reconstruction, and diagnostic capabilities.

Main Methods:

  • AI-driven automation in CT data acquisition, including patient positioning and parameter settings.
  • Optimization of image reconstruction parameters and advanced denoising techniques using AI.
  • Application of AI for automated organ segmentation and pathology detection in CT images.

Main Results:

  • AI enables significant reductions in patient radiation dose during CT scans.
  • Improved image quality, reduced noise, and enhanced diagnostic sensitivity through AI algorithms.
  • Clinical translation of AI methods for automation, increased accuracy, and new applications in patient care.

Conclusions:

  • AI advancements in CT lead to increased clinical benefit and decreased patient risk.
  • AI reduces radiation exposure and minimizes errors in CT examination performance and interpretation.
  • AI integration in CT enhances patient care through optimized imaging and diagnostics.