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Data-driven intensity normalization of PET group comparison studies is superior to global mean normalization.
Per Borghammer1, Joel Aanerud, Albert Gjedde
1PET Center, Aarhus University Hospitals, Denmark. per@pet.auh.dk
Neuroimage
|March 24, 2009
Summary
Global mean normalization is unreliable for neurodegenerative disorder studies. A data-driven reference cluster method significantly improves detection of cerebral blood flow changes compared to traditional methods.
Area of Science:
- Neuroimaging
- Nuclear Medicine
- Neurology
Background:
- Global mean (GM) normalization is standard for PET/SPECT neurodegenerative disorder studies.
- GM normalization assumes no between-group differences, often violated in these disorders.
- GM differences are hard to detect due to high data variance, necessitating alternative methods.
Purpose of the Study:
- To evaluate alternative normalization methods for neuroimaging in neurodegenerative disorders.
- To compare the efficacy of GM normalization against data-driven methods.
Main Methods:
- Simulations used cerebral blood flow (CBF) images from 49 controls.
- Cortical CBF was artificially reduced in simulated patient groups.
- Normalization methods included Global Mean, unbiased VOI, Andersson, and reference cluster methods.
Main Results:
- Global mean normalization recovered only a small percentage of the original signal and introduced artifacts.
- The data-driven reference cluster method successfully detected 65-95% of the original signal.
- Reference cluster methods proved superior in detecting simulated CBF changes.
Conclusions:
- The reference cluster method significantly outperformed GM normalization in simulations.
- This method is recommended for more accurate neuroimaging analysis in early to moderate neurodegenerative disorders.
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