Positron emission tomography (PET) with 2-(1-{6- [(2-[18F]fluoroethyl)(methyl)amino]-2-naphthyl}ethylidene)malo-nonitrile ([18F]FDDNP) has been used for in vivo imaging of amyloid plaques and neurofibrillary tangles, the neuopathological hallmarks of Alzheimer's disease (AD). The purpose of this study was to assess seven noninvasive methods for the estimation of the distribution volume ratio (DVR), as a measure of binding density of [18F]FDDNP in human brain, using a reference input (cerebellum). Imaging studies were performed on 20 AD and 13 normal control subjects. The image data were analyzed using two reference tissue modeling approaches with and without a physiological constraint on the efflux rate constant from reference tissue, Logan graphical analysis with and without a population efflux rate constant from reference tissue, and a simple concentration ratio between target and reference regions, to derive the DVR parameter. Highly linear correlations were observed among the DVR estimates except those by the tissue ratio approach which varied considerably. Reference tissue modeling approaches provided reliable estimation of DVR and gave further insight in the delivery of [18F]FDDNP to regions known to be affected by amyloid deposition. The efflux rate constant of cerebellum had minor effect on the DVR derived by Logan analysis. The [18F]FDDNP retention in subcortical white matter was similar between controls and ADs. Our results suggest that (1) Logan graphical analysis is the most robust method for quantitative analysis of [18F]FDDNP PET data without blood sampling, (2) reference tissue modeling approaches can provide further insight in helping to understand the transport properties of [18F]FDDNP and to aid model construction for kinetic analysis using the arterial input function, (3) efflux rate constant of the reference tissue can be derived with higher reliability by model fitting with physiological constraint, and (4) white matter may potentially be used as a reference region for analyzing [18F]FDDNP PET data.
[1]
T. Brubaker,et al.
Nonlinear Parameter Estimation
,
1979
.
[2]
H. Akaike.
A new look at the statistical model identification
,
1974
.
[3]
David Dagan Feng,et al.
Simultaneous estimation of physiological parameters and the input function - in vivo PET data
,
2001,
IEEE Transactions on Information Technology in Biomedicine.
[4]
Yonathan Bard,et al.
Nonlinear parameter estimation
,
1974
.
[5]
P. Thompson,et al.
PET of brain amyloid and tau in mild cognitive impairment.
,
2006,
The New England journal of medicine.
[6]
Peter J. Huber,et al.
Robust Statistics
,
2005,
Wiley Series in Probability and Statistics.
[7]
N. Volkow,et al.
Distribution Volume Ratios without Blood Sampling from Graphical Analysis of PET Data
,
1996,
Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.
[8]
Richard E Carson,et al.
Noise Reduction in the Simplified Reference Tissue Model for Neuroreceptor Functional Imaging
,
2002,
Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.
[9]
A. Lammertsma,et al.
Simplified Reference Tissue Model for PET Receptor Studies
,
1996,
NeuroImage.
[10]
D J Brooks,et al.
Comparison of Methods for Analysis of Clinical [11C]Raclopride Studies
,
1996,
Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.