RATIONALE AND OBJECTIVES: Radiotracers such as (11)C-PiB have enabled the in vivo imaging of amyloid-beta plaques in the brain, one of the histopathologic hallmarks of Alzheimer's disease (AD). Standardized uptake value ratio (SUVR) has become the most common normalization for (11)C-PiB as it does not require dynamic scans or blood sampling. Normalization is performed by computing the ratio of (11)C-PiB retention in the whole brain to that in cerebellar gray matter. However, SUVR is still conducted manually and is time consuming. An automated normalization algorithm is proposed. MATERIALS AND METHODS: Sixty participants from the Australian Imaging Biomarkers and Lifestyle (AIBL) study were used to test the developed algorithm and compare it against manual SUVR. The cohort consisted of participants likely to have AD (n = 20), those with mild cognitive impairment (MCI; n = 20), and normal controls (NC; n = 20). The participants underwent (11)C-PiB PET scans. A subset (n = 15) also underwent magnetic resonance imaging scans. (11)C-PET scans were segmented using an expectation maximization approach with inhomogeneity correction using three-dimensional cubic B-Splines. A cerebellar region was propagated and constrained by segmentation. Comparisons were made between manual and automated SUVR using regional analysis. Receiver-operating characteristic curves were computed for the task of AD-NC classification. Positron emission tomographic segmentations were also compared to co-registered magnetic resonance images of the same patient. RESULTS: Significant differences in regional means were observed between manual and automated SUVR. However, these changes were highly correlated (r > 0.8 for most regions). Significant differences (P < .05) in regional variances were also observed for the AD and NC subgroups. Area under the curve was 0.84 and 0.89 for manual and automated SUVR, respectively. CONCLUSIONS: The automated normalization technique results in less within-group variance and better discrimination between AD and NC participants.
RATIONALE AND OBJECTIVES: Radiotracers such as (11)C-PiB have enabled the in vivo imaging of amyloid-beta plaques in the brain, one of the histopathologic hallmarks of Alzheimer's disease (AD). Standardized uptake value ratio (SUVR) has become the most common normalization for (11)C-PiB as it does not require dynamic scans or blood sampling. Normalization is performed by computing the ratio of (11)C-PiB retention in the whole brain to that in cerebellar gray matter. However, SUVR is still conducted manually and is time consuming. An automated normalization algorithm is proposed. MATERIALS AND METHODS: Sixty participants from the Australian Imaging Biomarkers and Lifestyle (AIBL) study were used to test the developed algorithm and compare it against manual SUVR. The cohort consisted of participants likely to have AD (n = 20), those with mild cognitive impairment (MCI; n = 20), and normal controls (NC; n = 20). The participants underwent (11)C-PiB PET scans. A subset (n = 15) also underwent magnetic resonance imaging scans. (11)C-PET scans were segmented using an expectation maximization approach with inhomogeneity correction using three-dimensional cubic B-Splines. A cerebellar region was propagated and constrained by segmentation. Comparisons were made between manual and automated SUVR using regional analysis. Receiver-operating characteristic curves were computed for the task of AD-NC classification. Positron emission tomographic segmentations were also compared to co-registered magnetic resonance images of the same patient. RESULTS: Significant differences in regional means were observed between manual and automated SUVR. However, these changes were highly correlated (r > 0.8 for most regions). Significant differences (P < .05) in regional variances were also observed for the AD and NC subgroups. Area under the curve was 0.84 and 0.89 for manual and automated SUVR, respectively. CONCLUSIONS: The automated normalization technique results in less within-group variance and better discrimination between AD and NC participants.
Authors: Yi Su; Tyler M Blazey; Christopher J Owen; Jon J Christensen; Karl Friedrichsen; Nelly Joseph-Mathurin; Qing Wang; Russ C Hornbeck; Beau M Ances; Abraham Z Snyder; Lisa A Cash; Robert A Koeppe; William E Klunk; Douglas Galasko; Adam M Brickman; Eric McDade; John M Ringman; Paul M Thompson; Andrew J Saykin; Bernardino Ghetti; Reisa A Sperling; Keith A Johnson; Stephen P Salloway; Peter R Schofield; Colin L Masters; Victor L Villemagne; Nick C Fox; Stefan Förster; Kewei Chen; Eric M Reiman; Chengjie Xiong; Daniel S Marcus; Michael W Weiner; John C Morris; Randall J Bateman; Tammie L S Benzinger Journal: PLoS One Date: 2016-03-24 Impact factor: 3.240
Authors: Yi Su; Gina M D'Angelo; Andrei G Vlassenko; Gongfu Zhou; Abraham Z Snyder; Daniel S Marcus; Tyler M Blazey; Jon J Christensen; Shivangi Vora; John C Morris; Mark A Mintun; Tammie L S Benzinger Journal: PLoS One Date: 2013-11-06 Impact factor: 3.240
Authors: Elizabeth Morris; Anastasia Chalkidou; Alexander Hammers; Janet Peacock; Jennifer Summers; Stephen Keevil Journal: Eur J Nucl Med Mol Imaging Date: 2015-11-28 Impact factor: 9.236