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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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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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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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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 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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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Denoising of pediatric low dose abdominal CT using deep learning based algorithm.

Hyoung Suk Park1, Kiwan Jeon1, JeongEun Lee2,3

  • 1National Institute for Mathematical Sciences, Daejeon, Republic of Korea.

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This summary is machine-generated.

Deep learning generated virtual standard-dose CT images from low-dose CT scans, significantly reducing noise and improving image quality. This method offers an alternative to iterative reconstruction for older CT scanners.

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Low-dose computed tomography (LDCT) reduces radiation exposure but often suffers from increased image noise.
  • Standard-dose CT (SDCT) provides higher image quality but involves greater radiation risk.
  • Developing methods to enhance LDCT images to SDCT quality is crucial for diagnostic accuracy and patient safety.

Purpose of the Study:

  • To evaluate the efficacy of a deep learning (DL) method in generating standard-dose CT (SDCT)-like images from low-dose CT (LDCT) images.
  • To compare the performance of DL-generated virtual SDCT images (VIs) against original LDCT images (OIs) and traditional iterative reconstruction (IR).

Main Methods:

  • A generative adversarial network (GAN) framework was employed to train on unpaired LDCT and SDCT datasets.
  • Virtual SDCT images (VIs) were generated from LDCT images using the trained DL model.
  • Image quality was assessed by comparing VIs with OIs and SDCT images, and DL performance was compared with IR in a separate test set.

Main Results:

  • DL-generated VIs exhibited the lowest noise levels in both validation and test sets (p<0.001).
  • VIs demonstrated CT numbers similar to SDCT for the portal vein and liver, unlike OIs.
  • The contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of VIs were superior to SDCT, with VIs showing the highest SNR in test sets.

Conclusions:

  • The DL method effectively reduces noise in LDCT images, producing results comparable to SAFIRE iterative reconstruction.
  • This deep learning approach can be applied to enhance LDCT images from older CT scanners lacking advanced iterative reconstruction capabilities.