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Related Experiment Video

Updated: Feb 19, 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

Data-driven optimal binning for respiratory motion management in PET.

Adam L Kesner¹, Joseph G Meier², Darrell D Burckhardt³

¹Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Medical Physics

|November 3, 2017

Summary

This summary is machine-generated.

This study introduces a new patient-specific method for respiratory gating in PET imaging, creating optimal bin images that reduce motion blurring and improve image quality compared to traditional approaches.

Keywords:

adaptive imaging automatic binning data driven gated PET optimal binning personalized imaging

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Area of Science:

Medical Imaging
Nuclear Medicine
Image Processing

Background:

Respiratory gating in PET imaging minimizes motion-induced blurring.
Traditional optimal binning relies on population-derived assumptions.
A need exists for patient-specific motion characterization in PET.

Purpose of the Study:

To develop and validate a novel, data-driven strategy for patient-specific optimal binning in PET imaging.
To create patient-specific optimal bin images from gated PET scans.
To improve image quality and reduce motion artifacts in PET.

Main Methods:

Utilized 219 phase-gated FDG PET scans with data-driven gating.
Generated patient-specific phase-amplitude motion characterization using PCA.
Derived patient-specific optimal bin windows mirroring traditional strategies.
Validated optimal bin images via quantitative and qualitative measurements.

Main Results:

Optimal bin included 100% of image statistics in 53% of scans.
Optimal bin windows averaged 60% of statistics in remaining scans.
Optimal bin images showed improved resolution and noise characteristics compared to non-gated and gated images, respectively.
Lesion SUVmax values were 7.9 (non-gated), 8.5 (optimal bin), and 9.0 (gated).

Conclusions:

Extended optimal binning to a data-driven, patient-specific approach.
Automated strategy easily implemented in a large population.
Encapsulated motion information into an easy-to-use 3D image.
Simplicity and practicality suggest suitability for clinical settings.