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Related Concept Videos

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:
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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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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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STAN-CT: Standardizing CT Image using Generative Adversarial Networks.

Md Selim1,2, Jie Zhang3, Baowei Fei4,5

  • 1Department of Computer Science, University of Kentucky, Lexington, KY.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|May 3, 2021
PubMed
Summary

STAN-CT standardizes Computed Tomography (CT) images, reducing variations from different scanners and protocols. This improves large-scale radiomic studies for lung malignancy diagnostics and precision medicine.

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Area of Science:

  • Medical Imaging
  • Radiomics
  • Artificial Intelligence

Background:

  • Computed Tomography (CT) is vital for lung malignancy diagnostics and treatment assessment.
  • Variations in imaging protocols and scanners hinder large-scale, multi-center radiomic studies.

Purpose of the Study:

  • To develop an end-to-end solution, STAN-CT, for standardizing and normalizing CT images.
  • To reduce image feature discrepancies caused by diverse imaging protocols and scanners.

Main Methods:

  • STAN-CT utilizes a Generative Adversarial Network (GAN) with a latent-feature-based loss function.
  • An automated DICOM reconstruction pipeline with quality control generates standardized images.

Main Results:

  • STAN-CT significantly improves training efficiency and model performance.
  • It outperforms existing state-of-the-art CT image standardization algorithms.

Conclusions:

  • STAN-CT effectively addresses CT image standardization challenges in radiomics.
  • It enables more reliable and reproducible cross-center CT image analysis for lung cancer research.