Presentation Abstract

Session: Imaging Posters
Saturday, Jul 10, 2010, 11:15 AM - 1:15 PM
Presentation: IC-P-126 - Objective SUVr Determination using MRI Segmentation Maps in Florbetaben β-amyloid Brain PET Improves Discrimination of Alzheimer’s and Controls
Pres. Time: Saturday, Jul 10, 2010, 12:15 PM - 1:15 PM
Location: 313-C
Category: +New imaging methods
Author(s): John Seibyl, MD1, Olivier Barret, PhD1, George Zubal, PhD1, Florian Hiemeyer, PhD2, Cornelia Reininger, MD PhD2, Henryk Barthel, MD3, Jeff Batis, PhD1, Gary Wisniewski1, Osama Sabri, MD3.
1Institute for Neurodegenerative Disorders, New Haven, CT, USA, 2Bayer Healthcare, Berlin, Germany, 3University of Leipzig, Leipzig, Germany.
Abstract: Background:
Regional quantitative estimates of brain β-amyloid burden employing 18F-amyloid PET tracers like florbetaben and SUV ratios (SUVr) are important outcome measures in diagnostic and therapeutic trials in Alzheimer’s disease (AD). White matter uptake may spuriously affect SUVrs of neocortical regions to cerebellar grey matter, thereby reducing discrimination between AD subjects and healthy volunteers (HVs). The aim of this analysis - included in a multicenter Phase 2 trial - was to determine the value of individualized grey and white matter segmentation maps on florbetaben PET SUVr measures compared to a standard VOI assessment.
146 subjects imaged with florbetaben PET were included (54% patients with probable AD (age=55yrs, MMSE=18-26, CDR=0.5-2) and 46% age-matched HVs (MMSE=28, CDR=0)). PET. Each subject’s PET and T1 weighted volumetric MRI was spatially normalized and MRIs segmented into grey and white matter maps. These maps were overlaid onto the florbetaben PET image to create composite images upon which a modified AAL VOI template was placed and non-null voxels were sampled across cortical and subcortical regions to generate SUVs and SUVrs. By means of mixed effects ANOVA and linear discriminant function models, the SUV data was compared to that generated with a template not implementing this type of segmentation.
Both segmented and standard VOI analyses revealed significantly (p<0.0001) higher SUVRs for the ADs compared to HVs in the frontal, laterotemporal, parietal, and posterior cingulate, and other cortical regions. The segmentation analysis resulted in 10-22% improved discrimination between AD and HVs (p<0.0001) for cortical regions compared to standard SUV assessment. Optimizing for accuracy in the discriminant function revealed that implementation of segmentation resulted in an overall sensitivity = 86%, specificity = 91%, and accuracy = 88%. This represents an improvement over the standard VOI analysis (accuracy= 81%).
This preliminary data, applying individualized, MRI-based grey and white matter map in SUVr analysis of florbetaben PET images, improves quantitative discrimination between AD and HVs using an objective, rapid and user-independent technique.
Disclosures:   J. Seibyl, Eli Lilly; Bristol Meyers Squibb; Sepracor; Merck Serono; Bayer Healthcare; GE Healthcare; Molecular Neuroimaging; O. Barret, Molecular Neuroimaging; G. Zubal, Molecular Neuroimaging; F. Hiemeyer, Bayer Healthcare; C. Reininger, Bayer Healthcare; H. Barthel, Bayer Healthcare; J. Batis, Molecular Neuroimaging; G. Wisniewski, Molecular Neuroimaging; O. Sabri, Bayer Healthcare; Bayer Healthcare.
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