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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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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Related Experiment Video

Updated: Jul 3, 2026

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

Fast noise reduction in computed tomography for improved 3-D visualization.

Michiel Schaap1, Arnold M R Schilham, Karel J Zuiderveld

  • 1Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands. michiel.schaap@erasmusmc.nl

IEEE Transactions on Medical Imaging
|August 2, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a faster anisotropic smoothing framework for computed tomography (CT) scans. The new method improves noise reduction while preserving crucial small details in medical imaging.

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Area of Science:

  • Medical Imaging
  • Image Processing
  • Computational Science

Background:

  • Computed tomography (CT) imaging trends towards higher resolution, increasing noise levels.
  • Anisotropic smoothing techniques are crucial for noise reduction in CT while preserving important structures.
  • Existing smoothing methods are often too slow for clinical use and struggle with preserving fine details.

Purpose of the Study:

  • To present a speed-optimized framework for anisotropic smoothing in CT.
  • To enhance existing smoothing techniques for better preservation of small structures in CT data.
  • To improve the clinical applicability of anisotropic smoothing for medical imaging.

Main Methods:

  • Development of a widely applicable, speed-optimized framework for anisotropic smoothing.
  • Extension of an existing smoothing technique using an importance map based on second-order image structure.
  • Anisotropic diffusion process applied, preserving structures with significant second-order information.

Main Results:

  • Qualitative evaluation through an observer study assessing 3-D CT visualization improvements.
  • Quantitative assessment by measuring the reduction in differences between low-dose and high-dose CT scans of carotid plaques.
  • Demonstrated improvement in preserving small structures compared to methods without structure preservation.

Conclusions:

  • The presented framework offers a significant speed improvement for anisotropic smoothing in CT.
  • The enhanced technique effectively preserves small, diagnostically relevant structures in CT images.
  • This work facilitates more efficient and detailed noise reduction in medical CT imaging.