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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...

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Related Experiment Video

Updated: Jun 3, 2026

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

A multiple points method for 4D CT image sorting.

Chiara Gianoli1, Marco Riboldi, Maria Francesca Spadea

  • 1Department of Bioengineering, TBM Laboratory Politecnico di Milano, Milano 20133, Italy. chiara.gianoli@mail.polimi.it

Medical Physics
|April 2, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using multiple breathing signals to reduce artifacts in four-dimensional computed tomography (4D CT) images. The technique improves breathing phase identification, especially with irregular breathing patterns, leading to clearer images.

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Biomedical Engineering

Background:

  • Artifacts in four-dimensional computed tomography (4D CT) images arise from breathing irregularities and inaccurate breathing phase identification.
  • These artifacts can compromise image quality and diagnostic accuracy.

Purpose of the Study:

  • To reduce artifacts in sorted 4D CT images.
  • To enhance the robustness and accuracy of breathing phase identification using multiple respiratory signals.

Main Methods:

  • Utilized infrared 3D localization of thoracoabdominal surface markers to capture multiple respiratory signals.
  • Employed multidimensional K-means clustering for retrospective 4D CT image sorting based on multiple marker variables.
  • Validated the technique using computational simulations, phantom experiments, and clinical patient data.

Main Results:

  • The multidimensional clustering technique consistently improved breathing phase identification compared to conventional monodimensional methods, particularly with breathing irregularities.
  • Artifact reduction was clearly observed in clinical 4D CT images, especially in the lower lung regions.
  • Evaluated performance through repeatability and cycle sampling uniformity in simulations and phantom studies.

Conclusions:

  • The developed multiple-point method effectively reduces artifacts in 4D CT imaging.
  • Further research is recommended to optimize the use of multiple respiratory variables and extend the method to 4D CT-PET hybrid scans.