Alzheimer's disease detection using 11C-PiB with improved partial volume effect correction

Despite the increasing use of "C-PiB in research into Alzheimer's disease (AD), there are few standardized analysis procedures that have been reported or published. This is especially true with regards to partial volume effects (PVE) and partial volume correction. Due to the nature of PET physics and acquisition, PET images exhibit relatively low spatial resolution compared to other modalities, resulting in bias of quantitative results. Although previous studies have applied PVE correction techniques on 11 C-PiB data, the results have not been quantitatively evaluated and compared against uncorrected data. The aim of this study is threefold. Firstly, a realistic synthetic phantom was created to quantify PVE. Secondly, MRI partial volume estimate segmentations were used to improve voxel-based PVE correction instead of using hard segmentations. Thirdly, quantification of PVE correction was evaluated on 34 subjects (AD=10, Normal Controls (NC) =24), including 12 PiB positive NC. Regional analysis was performed using the Anatomical Automatic Labeling (AAL) template, which was registered to each patient. Regions of interest were restricted to the gray matter (GM) defined by the MR segmentation. Average normalized intensity of the neocortex and selected regions were used to evaluate the discrimination power between AD and NC both with and without PVE correction. Receiver Operating Characteristic (ROC) curves were computed for the binary discrimination task. The phantom study revealed signal losses due to PVE between 10 to 40 % which were mostly recovered to within 5% after correction. Better classification was achieved after PVE correction, resulting in higher areas under ROC curves.