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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.
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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

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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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Computed Tomography (CT) scan:
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Imaging Studies for Cardiovascular System V: CT01:28

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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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Positron Emission Tomography01:29

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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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Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
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Related Experiment Video

Updated: Nov 30, 2025

High Resolution 3D Imaging of Ex-Vivo Biological Samples by Micro CT
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High Resolution 3D Imaging of Ex-Vivo Biological Samples by Micro CT

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Low-dose CT image and projection dataset.

Taylor R Moen1, Baiyu Chen1, David R Holmes2

  • 1Department of Radiology, Mayo Clinic, Rochester, MN, USA.

Medical Physics
|November 17, 2020
PubMed
Summary
This summary is machine-generated.

A new computed tomography (CT) dataset offers projection data at standard and low doses for developing advanced image reconstruction and AI algorithms. This resource aids in validating new CT technologies for improved diagnostic performance.

Keywords:
CT projection dataiterative reconstructionlow-dose CTmachine learningpatient data

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Area of Science:

  • Medical Imaging
  • Radiology
  • Computer Science

Background:

  • Computed tomography (CT) is crucial for medical diagnosis.
  • Developing advanced CT reconstruction and denoising algorithms is essential for improving image quality and reducing radiation dose.
  • Publicly available datasets are vital for reproducible research and algorithm validation.

Purpose of the Study:

  • To introduce a large, publicly accessible dataset of computed tomography (CT) projection data.
  • The dataset includes data from routine clinical doses and simulated lower doses.
  • To facilitate the development and validation of novel CT reconstruction and denoising algorithms.

Main Methods:

  • Collected projection and image data from 299 patient CT exams across three clinical scenarios (head, chest, abdomen).
  • Acquired data using CT systems from two manufacturers under routine protocols.
  • Simulated reduced-dose projection data using a validated noise-insertion method and validated data with reconstruction algorithms and a prior challenge.
  • Archived data in DICOM-CT-PD format for projections and standard DICOM for images, with accompanying clinical data and usage tools.

Main Results:

  • The dataset comprises projection and image data from 299 patient CT examinations.
  • Includes data acquired at routine clinical doses and simulated lower doses.
  • Projection data were validated through reconstruction and participation in the 2016 Low Dose CT Grand Challenge.
  • Radiologists provided annotations for pathologies, with reference truth from medical records.

Conclusions:

  • The CT data library supports the advancement of CT reconstruction and denoising algorithms, including AI-based methods.
  • Enables the evaluation of task-based diagnostic performance using provided clinical information.
  • Promotes the development of next-generation CT imaging techniques.