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Related Experiment Video

Updated: May 11, 2026

Management of Respiratory Motion Artefacts in 18F-fluorodeoxyglucose Positron Emission Tomography using an Amplitude-Based Optimal Respiratory Gating Algorithm

Management of Respiratory Motion Artefacts in ¹⁸F-fluorodeoxyglucose Positron Emission Tomography using an Amplitude-Based Optimal Respiratory Gating Algorithm

Published on: July 23, 2020

Using a registration-based motion correction algorithm to correct for respiratory motion during myocardial perfusion

Shelley Redgate¹, David C Barber, Abdallah Al-Mohammad

¹Department of Nuclear Medicine, Sheffield Teaching Hospitals NHS Foundation Trust, University of Sheffield, South Yorkshire, UK. shelley.redgate@sth.nhs.uk

Nuclear Medicine Communications

|June 1, 2013

Summary

A new algorithm effectively reduces respiratory motion artifacts in myocardial perfusion imaging. This motion correction method improves image quality for displacements of 2 cm or more.

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Last Updated: May 11, 2026

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Published on: August 17, 2022

Area of Science:

Medical Imaging
Cardiovascular Imaging
Image Processing

Background:

Respiratory motion significantly degrades the quality of myocardial perfusion imaging.
Motion artifacts can obscure important diagnostic information in cardiac scans.

Purpose of the Study:

To develop and assess a registration-based algorithm for reducing respiratory motion artifacts.
To evaluate the algorithm's efficacy in improving myocardial perfusion imaging.

Main Methods:

Utilized NCAT software for computer simulations of myocardial perfusion imaging with varying respiratory motion and ejection fractions.
Implemented a nonlinear registration algorithm to align images within projections for motion correction.
Compared corrected images against a standard simulation without respiratory motion.

Main Results:

Uncorrected motion caused mean segmental count differences of 3%, 14%, and 28% for 1, 2, and 3 cm displacements, respectively.
Motion correction reduced these differences to 2%, 5%, and 7% for the same displacements.
The algorithm slightly reduced mean counts by 3% across all segments.

Conclusions:

The developed registration-based algorithm effectively improves myocardial perfusion images affected by respiratory motion.
The algorithm demonstrates significant benefit for motion displacements of 2 cm and greater.