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Computed Tomography01:10

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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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Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences
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SU-E-I-95: Multiscale Normalized Metal Artifact Reduction (MS-NMAR) in Computed Tomography.

S Tang1, X Tang1

  • 1Emory University School of Medicine, Atlanta, Georgia.

Medical Physics
|May 19, 2017
PubMed
Summary
This summary is machine-generated.

A new multiscale normalized metal artifact reduction (MS-NMAR) method improves CT image quality by reducing metal artifacts more effectively than conventional methods. MS-NMAR outperforms normalized MAR (NMAR) by minimizing new artifact generation.

Keywords:
Composite materialsComputed tomographyInterpolationMedical image artifactsMedical image qualityMedical image reconstructionMedical imagingUltrasonography

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Area of Science:

  • Medical Imaging
  • Computed Tomography (CT)
  • Image Reconstruction

Background:

  • Medical X-ray CT devices produce high-quality images, but metal artifacts degrade image quality.
  • Conventional metal artifact reduction (MAR) methods often introduce new artifacts by not utilizing prior image information.
  • Normalized MAR (NMAR) improves accuracy but is less effective for small-scale details.

Purpose of the Study:

  • To introduce a novel multiscale normalized metal artifact reduction (MS-NMAR) method.
  • To address the limitations of NMAR in handling small-scale details and reducing artifacts.
  • To enhance the accuracy and quality of CT images in the presence of metal implants.

Main Methods:

  • MS-NMAR generalizes NMAR using a multiscale framework, decomposing projection data and prior images into different scales.
  • Normalization and linear interpolation are applied at each scale, with wider intervals for larger scales.
  • Composite projection data are formed by summing components across all scales for image reconstruction.

Main Results:

  • The efficiency of MS-NMAR was verified using real CT data.
  • MS-NMAR demonstrated superior performance compared to conventional MAR and NMAR.
  • Image quality was evaluated by inspecting regions around metal materials.

Conclusions:

  • Both NMAR and MS-NMAR significantly outperform conventional MAR.
  • MS-NMAR offers an advantage over NMAR by preventing the creation of new artifacts.
  • The proposed MS-NMAR method effectively reduces metal artifacts while preserving image details.