UNLABELLED: The availability of new PET ligands offers the potential to measure fibrillar β-amyloid in the brain. Nevertheless, physiologic information in the form of perfusion or metabolism may still be useful in differentiating causes of dementia during life. In this study, we investigated whether early (11)C-Pittsburgh compound B ((11)C-PIB) PET frames (perfusion (11)C-PIB [pPIB]) could provide information equivalent to blood flow and metabolism. First, we assessed the similarity of pPIB and (18)F-FDG PET images in a test cohort with various clinical diagnoses (n = 10), and then we validated the results in a cohort of patients with Alzheimer disease (AD) (n = 42; mean age ± SD, 66.6 ± 10.6 y; mean Mini-Mental State Examination [MMSE] score ± SD, 22.2 ± 6.0) or frontotemporal lobar degeneration (FTLD) (n = 31; age ± SD, 63.9 ± 7.1 y, mean MMSE score ± SD, 23.8 ± 6.7). METHODS: To identify the (11)C-PIB frames best representing perfusion, we ran on a test cohort an iterative algorithm, including generating normalized (cerebellar reference) perfusion pPIB images across variable frame ranges and calculating Pearson R values of the sum of these pPIB frames with the sum of all (18)F-FDG frames (cerebellar normalized) for all brain tissue voxels. Once this perfusion frame range was determined on the test cohort, it was then validated on an extended cohort and the power of pPIB in differential diagnosis was compared with (18)F-FDG by performing a logistic regression of regions-of-interest tracer measure (pPIB or (18)F-FDG) versus diagnosis. RESULTS: A 7-min window, corresponding to minutes 1-8 (frames 5-15), produced the highest voxelwise correlation between (18)F-FDG and pPIB (R = 0.78 ± 0.05). This pPIB frame range was further validated on the extended AD and FTLD cohort across 12 regions of interest (R = 0.91 ± 0.09). A logistic model using pPIB was able to classify 90.5% of the AD and 83.9% of the FTLD patients correctly. Using (18)F-FDG, we correctly classified 88.1% of AD and 83.9% of FTLD patients. The temporal pole and temporal neocortex were significant discriminators (P < 0.05) in both models, whereas in the model with pPIB the frontal region was also significant. CONCLUSION: The high correlation between pPIB and (18)F-FDG measures and their comparable performance in differential diagnosis are promising in providing functional information using (11)C-PIB PET data. This approach could be useful, obviating (18)F-FDG scans when longer-lived amyloid imaging agents become available.
UNLABELLED: The availability of new PET ligands offers the potential to measure fibrillar β-amyloid in the brain. Nevertheless, physiologic information in the form of perfusion or metabolism may still be useful in differentiating causes of dementia during life. In this study, we investigated whether early (11)C-Pittsburgh compound B ((11)C-PIB) PET frames (perfusion (11)C-PIB [pPIB]) could provide information equivalent to blood flow and metabolism. First, we assessed the similarity of pPIB and (18)F-FDG PET images in a test cohort with various clinical diagnoses (n = 10), and then we validated the results in a cohort of patients with Alzheimer disease (AD) (n = 42; mean age ± SD, 66.6 ± 10.6 y; mean Mini-Mental State Examination [MMSE] score ± SD, 22.2 ± 6.0) or frontotemporal lobar degeneration (FTLD) (n = 31; age ± SD, 63.9 ± 7.1 y, mean MMSE score ± SD, 23.8 ± 6.7). METHODS: To identify the (11)C-PIB frames best representing perfusion, we ran on a test cohort an iterative algorithm, including generating normalized (cerebellar reference) perfusion pPIB images across variable frame ranges and calculating Pearson R values of the sum of these pPIB frames with the sum of all (18)F-FDG frames (cerebellar normalized) for all brain tissue voxels. Once this perfusion frame range was determined on the test cohort, it was then validated on an extended cohort and the power of pPIB in differential diagnosis was compared with (18)F-FDG by performing a logistic regression of regions-of-interest tracer measure (pPIB or (18)F-FDG) versus diagnosis. RESULTS: A 7-min window, corresponding to minutes 1-8 (frames 5-15), produced the highest voxelwise correlation between (18)F-FDG and pPIB (R = 0.78 ± 0.05). This pPIB frame range was further validated on the extended AD and FTLD cohort across 12 regions of interest (R = 0.91 ± 0.09). A logistic model using pPIB was able to classify 90.5% of the AD and 83.9% of the FTLDpatients correctly. Using (18)F-FDG, we correctly classified 88.1% of AD and 83.9% of FTLDpatients. The temporal pole and temporal neocortex were significant discriminators (P < 0.05) in both models, whereas in the model with pPIB the frontal region was also significant. CONCLUSION: The high correlation between pPIB and (18)F-FDG measures and their comparable performance in differential diagnosis are promising in providing functional information using (11)C-PIB PET data. This approach could be useful, obviating (18)F-FDG scans when longer-lived amyloid imaging agents become available.
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