• Catharina Lange, Per Suppa, Lars Frings, Winfried Brenner, Lothar Spies, and Ralph Buchert.
• Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany.
• J. Alzheimers Dis. 2016 Jan 1; 49 (4): 945-959.

BackgroundPositron emission tomography (PET) with the glucose analog F-18-fluorodeoxyglucose (FDG) is widely used in the diagnosis of neurodegenerative diseases. Guidelines recommend voxel-based statistical testing to support visual evaluation of the PET images. However, the performance of voxel-based testing strongly depends on each single preprocessing step involved.ObjectiveTo optimize the processing pipeline of voxel-based testing for the prognosis of dementia in subjects with amnestic mild cognitive impairment (MCI).MethodsThe study included 108 ADNI MCI subjects grouped as 'stable MCI' (n = 77) or 'MCI-to-AD converter' according to their diagnostic trajectory over 3 years. Thirty-two ADNI normals served as controls. Voxel-based testing was performed with the statistical parametric mapping software (SPM8) starting with default settings. The following modifications were added step-by-step: (i) motion correction, (ii) custom-made FDG template, (iii) different reference regions for intensity scaling, and (iv) smoothing was varied between 8 and 18 mm. The t-sum score for hypometabolism within a predefined AD mask was compared between the different settings using receiver operating characteristic (ROC) analysis with respect to differentiation between 'stable MCI' and 'MCI-to-AD converter'. The area (AUC) under the ROC curve was used as performance measure.ResultsThe default setting provided an AUC of 0.728. The modifications of the processing pipeline improved the AUC up to 0.832 (p = 0.046). Improvement of the AUC was confirmed in an independent validation sample of 241 ADNI MCI subjects (p = 0.048).ConclusionThe prognostic value of voxel-based single subject analysis of brain FDG PET in MCI subjects can be improved considerably by optimizing the processing pipeline.

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