OO015 - IMPROVED SUVR CALCULATION FOR [18F]-AV45 AMYLOID PET IMAGING USING A NOVEL REFERENCE REGION APPROACH (ID 1215)
Abstract
Aims
Utilization of a data-driven approach to find an optimal reference region for [18F]-AV45 PET imaging that differentiates the spectrum of Alzheimer’s disease (AD) in both cross-sectional and longitudinal study designs.
Methods
Data of 283 participants (135 amyloid-negative cognitively normal (CN), and 148 amyloid-positive AD) from the ADNI database (http://adni.loni.usc.edu/) was used. All [18F]-AV45 scans were co-registered, normalized, and skull-stripped. The dataset was split into a training-and test-dataset. Voxel-wise group comparisons were performed in the training-set (75 CNs, 77 ADs) to identify a reference region that is void off on-target tracer uptake. Potential clusters were used to extract mean global SUVRs in the test-dataset (60 CNs, 70 ADs). Effect sizes between novel clusters and commonly used reference regions were compared. Baseline and follow-up data of 19 CNs, 36 participants with mild cognitive impairment, and 24 ADs was used to test whether the newly identified cluster is more sensitive to assess longitudinal change than common reference regions. Effect sizes of change in SUVR between baseline and follow-up were used as metric of sensitivity.
Results
The training dataset yielded two novel clusters in the brainstem and the cerebellar white matter. These new reference regions showed higher effect sizes compared with commonly used reference regions. Significant differences in the effect sizes were observed when examining longitudinal change in SUVR computation compared with previously used reference regions.
Conclusions
The data-driven approach using both cross-sectional and longitudinal study designs improved SUVR measurements for [18]-AV45 imaging. Additionally, longitudinal SUVR quantification benefited from this method, with implications for clinical trial designs.
