Objective: Automated voxel-based analysis methods are used to detect cortical hypometabolism
typical of Alzheimer’s Disease (AD) on FDG-PET brain scans. We compared the accuracy of two
clinically validated tools for their ability to identify those MCI subjects progressing to AD at followup,
to evaluate the impact of the analysis method on FDG-PET diagnostic performance.
Methods: SPMGrid and BRASS (Hermes Medical Solutions, Stockholm, Sweden) were tested on 131
MCI and elderly healthy controls from the EADC PET dataset. The concordance between the tools was
tested by correlating the quantitative parameters (z- and t-values), calculated by the two software tools,
and by measuring the topographical overlap of the abnormal regions (Dice score). Three independent
expert readers blindly assigned a diagnosis based on the two map sets. We used conversion to AD
dementia as the gold standard.
Results: The t-map and z-map calculated with SPMGrid and BRASS, respectively, showed a good correlation
(R > .50) for the majority of individual cases (128/131) and for the majority of selected regions of
interest (ROIs) (98/116). The overlap of the hypometabolic patterns from the two tools was, however,
poor (Dice score .36). The diagnostic performance was comparable, with BRASS showing significantly
higher sensitivity (.82 versus .59) and SPMGrid showing higher specificity (.87 versus .52).
Conclusion: Despite similar diagnostic performance in predicting conversion to AD in MCI subjects,
the two tools showed significant differences, and the maps provided by the tools showed limited overlap.
These results underline the urgency for standardization across FDG-PET analysis methods for
their use in clinical practice.