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Related Concept Videos

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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Planar Rigid-Body Motion01:22

Planar Rigid-Body Motion

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Curvilinear Motion: Rectangular Components

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Related Experiment Video

Updated: Jun 20, 2026

Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

A scheme for PET data normalization in event-based motion correction.

Victor W Zhou1, Andre Z Kyme, Steven R Meikle

  • 1School of Physics, University of Sydney, NSW 2006, Sydney, Australia.

Physics in Medicine and Biology
|August 19, 2009
PubMed
Summary
This summary is machine-generated.

Proper normalization during Line of Response (LOR) rebinning for positron emission tomography (PET) motion correction is crucial. Accounting for multiple LORs per sinogram bin prevents image artifacts in PET scans.

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Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Image Reconstruction

Background:

  • Line of Response (LOR) rebinning is an effective event-based motion correction technique for positron emission tomography (PET).
  • Previous methods required normalization factors based on original LOR detector pairs.
  • Data compression (mashing) leads to sinogram bins containing events from multiple LORs.

Purpose of the Study:

  • To thoroughly investigate and detail event-based normalization strategies during LOR rebinning for PET motion correction.
  • To demonstrate the necessity of accounting for the number of LORs per sinogram bin for accurate normalization.
  • To prevent image artifacts caused by inadequate normalization during motion correction.

Main Methods:

  • Developed a theoretical framework for event-based normalization during LOR rebinning.
  • Implemented and experimentally validated the proposed normalization method using phantom studies.
  • Analyzed the impact of LOR multiplicity within sinogram bins on normalization factors.

Main Results:

  • Normalization must account for the number of LORs contributing to a sinogram bin after motion correction.
  • Failure to do so results in artifactual slice-to-slice count variations and horizontal stripe artifacts.
  • Experimental phantom studies confirmed the theoretical predictions and the efficacy of the proposed method.

Conclusions:

  • Accurate event-based normalization during LOR rebinning is essential for high-quality PET imaging.
  • The proposed normalization method effectively corrects for motion-induced artifacts.
  • This work provides a detailed guide for implementing improved normalization in PET motion correction.