Data-driven intensity normalization of PET group comparison studies is superior to global mean normalization

Abstract

Background: Global mean (GM) normalization is one of the most commonly used methods of normalization in PET and SPECT group comparison studies of neurodegenerative disorders. It requires that no between-group GM difference is present, which may be strongly violated in neurodegenerative disorders. Importantly, such GM differences often elude detection due to the large intrinsic variance in absolute values of cerebral blood flow or glucose consumption. Alternative methods of normalization are needed for this type of data.

Materials and methods: Two types of simulation were performed using CBF images from 49 controls. Two homogeneous groups of 20 subjects were sampled repeatedly. In one group, cortical CBF was artificially decreased moderately (simulation I) or slightly (simulation II). The other group served as controls. Ratio normalization was performed using five reference regions: (1) Global mean; (2) An unbiased VOI; (3) Data-driven region extraction (Andersson); (4-5) Reference cluster methods (Yakushev et al.). Using voxel-based statistics, it was determined how much of the original signal was detected following each type of normalization.

Results: For both simulations, global mean normalization performed poorly, with only a few percent of the original signal recovered. Global mean normalization moreover created artificial increases. In contrast, the data-driven reference cluster method detected 65-95% of the original signal.

Conclusion: In the present simulation, the reference cluster method was superior to GM normalization. We conclude that the reference cluster method will likely yield more accurate results in the study of patients with early to moderate stage neurodegenerative disorders.