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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.
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IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
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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.
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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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Updated: Nov 9, 2025

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Basic of machine learning and deep learning in imaging for medical physicists.

Luigi Manco1, Nicola Maffei1, Silvia Strolin2

  • 1A.O. U. di Modena, Medical Physics Unit, Modena, Italy.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)
|April 7, 2021
PubMed
Summary

This study reviews artificial intelligence (AI) algorithms and automation tools for healthcare imaging. It analyzes AI applications in various medical fields and imaging types over five years, discussing current limitations and future opportunities.

Keywords:
Artificial intelligenceImagingMachine Learningdeep Learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Artificial intelligence (AI) is increasingly applied to medical imaging analysis.
  • A comprehensive overview of AI algorithms and automation tools in healthcare imaging is needed.
  • Understanding the current landscape of AI in medical imaging is crucial for future development.

Purpose of the Study:

  • To provide an overview of published AI algorithms and automation tools for healthcare imaging.
  • To analyze the distribution of AI applications across medical disciplines and image types.
  • To discuss the limitations and opportunities of AI in clinical practice and future research.

Main Methods:

  • A systematic PubMed search was conducted using a specific query string.
  • The search focused on AI (machine and deep learning) approaches in medical imaging.
  • Data were collected over a 5-year period to identify relevant algorithms and tools.

Main Results:

  • The study presents the distribution of AI applications across various medical disciplines.
  • It details the types of medical images investigated in conjunction with AI approaches.
  • An analysis of identified algorithms and automation tools is provided.

Conclusions:

  • AI shows significant potential in healthcare imaging, with diverse applications emerging.
  • Current limitations in clinical practice need to be addressed for wider AI adoption.
  • Future research should focus on overcoming these limitations and exploring new opportunities for AI in medical imaging.