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Positron Emission Tomography

Positron Emission Tomography

Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...

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Related Experiment Video

Updated: Dec 12, 2025

Radiotracer Administration for High Temporal Resolution Positron Emission Tomography of the Human Brain: Application to FDG-fPET

Radiotracer Administration for High Temporal Resolution Positron Emission Tomography of the Human Brain: Application to FDG-fPET

Published on: October 22, 2019

Intensity normalization methods in brain FDG-PET quantification.

Francisco J López-González¹, Jesús Silva-Rodríguez², José Paredes-Pacheco¹

¹Molecular Imaging Group, Radiology Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain; Molecular Imaging Unit, Centro de Investigaciones Médico-Sanitarias, General Foundation of the University of Málaga, Málaga, Spain.

|August 11, 2020

Summary

This summary is machine-generated.

Standardizing brain FDG-PET quantification is crucial. This study evaluated intensity normalization methods, recommending histogram-based approaches for accurate hypometabolism detection and minimizing false positives in FDG-PET imaging.

Keywords:

FDG-PET Intensity normalization Monte Carlo SPM

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Last Updated: Dec 12, 2025

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Area of Science:

Neuroimaging
Medical Physics
Quantitative Analysis

Background:

Brain FDG-PET quantification lacks standardized intensity normalization methods.
This variability impacts the reliability and harmonization of quantification protocols.
The effect of different normalization techniques on quantification output remains unclear.

Purpose of the Study:

To perform a ground truth-based evaluation of various intensity normalization methods.
To assess their impact on brain FDG-PET quantification outcomes.
To identify optimal methods for harmonized quantification.

Main Methods:

Generated realistic brain FDG-PET images using Monte Carlo simulations with controlled hypometabolism.
Applied single-subject statistical parametric mapping (SPM) after intensity normalization.
Evaluated reference region methods (RRBS, RRC, RRTL) and data-driven methods (PS, HN, iPS, iHN).

Main Results:

All methods underestimated hypometabolic volumes, especially for milder reductions.
Cerebellum (RRC) and histogram-based (HN) methods recovered the largest volumes.
Data-driven methods generally outperformed reference region methods, with iterative versions showing better recovery.

Conclusions:

Inappropriate intensity normalization introduces significant bias and false positives in hypometabolism detection.
Histogram-based methods are recommended for their accuracy.
Reference region methods are only comparable to data-driven methods with large, stable reference regions.