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Related Experiment Video

Updated: Sep 16, 2025

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Data-Driven Image-Based Protocol for Brain PET Image Harmonization.

Eva Štokelj1, Urban Simončič1,2, For The Alzheimer's Disease Neuroimaging Initiative

  • 1Faculty of Mathematics and Physics, University of Ljubljana, 1000 Ljubljana, Slovenia.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel method to harmonize multi-center Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) brain imaging data. The approach effectively reduces scanner variability, improving data comparability for research, especially when phantom scans are unavailable.

Area of Science:

  • Neuroimaging
  • Medical Physics
  • Radiochemistry

Background:

  • Quantitative FDG-PET brain imaging faces challenges due to inter-scanner variability, hindering data comparability across research centers.
  • Traditional harmonization methods often rely on phantom scans, which may not always be feasible or available for retrospective studies.

Purpose of the Study:

  • To develop and validate a data-driven, image-based harmonization protocol for quantitative FDG-PET brain imaging.
  • To address inter-scanner variability without the need for traditional phantom scans.

Main Methods:

  • The protocol estimates scanner-specific Gaussian smoothing filters using spatially normalized FDG-PET brain images.
  • Optimization of filter parameters was achieved using the structural similarity index (SSIM).
Keywords:
FDG-PETbrain imagingimage harmonizationmulticenter studies

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  • Validation utilized data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including cognitively normal individuals and Alzheimer's disease patients.

    • Main Results:

    • Robust harmonization was achieved at moderate resolutions (8 and 10 mm FWHM), with estimated filters closely matching phantom-derived ground truths (within 1.2 mm).
    • At higher resolutions (6 mm FWHM), discrepancies increased up to 3 mm, particularly for high-resolution scanners like HRRT, due to noise and smaller sample sizes.
    • The method effectively reduces inter-scanner variability in retrospective FDG-PET studies.

    Conclusions:

    • The proposed image-based harmonization protocol is a valuable tool for reducing inter-scanner variability in retrospective FDG-PET studies, especially when phantom data is absent.
    • Methodological refinements are necessary to improve accuracy at finer resolutions and accommodate advanced high-resolution PET imaging technologies.