Amyloid-β imaging with PET in Alzheimer’s disease:
is it feasible with current radiotracers and technologies?
Mateen C. Moghbel & Babak Saboury & Sandip Basu &
Scott D. Metzler & Drew A. Torigian &
Bengt Långström & Abass Alavi
Published online: 19 October 2011
# Springer-Verlag 2011
Although it afflicts an estimated 26.6 million people
worldwide—a figure that is expected to quadruple by
2050—Alzheimer’s disease (AD) has yet to be fully
understood etiologically, diagnostically, or therapeutically
. For decades, the most widely accepted definite
diagnosis of AD has been the histological observation of
senile plaques composed of amyloid-β (Aβ) and neurofi-
brillary tangles comprising tau [2–4]. Theories abound as to
the mechanisms behind these deposits of Aβ and tau, one
of the most prominent of which is the “amyloid hypothesis.”
This hypothesis proposes that the cleavage of amyloid
precursor protein by β-secretase and γ-secretase causes
Aβ42 to accumulate as senile plaques, which results in
synaptic and neuronal injury .
The credence afforded to the amyloid hypothesis has
spurred the development of a number of tracers intended to
reflect the burden of amyloid plaques in AD patients in vivo
and non-invasively with positron emission tomography
(PET). The earliest amyloid imaging agents, including [11C]
PiB and [18F]FDDNP, were designed and tested in the early-
to-mid part of the last decade, and have been limited to
research studies. However, the advent of three new radio-
tracers, which are currently at various stages of FDA
assessment and approval, has brought amyloid imaging to
the doorstep of clinical use. Recent clinical studies on
florbetapir (AV-45), florbetaben (BAY-94), and flutemetamol
(GE-067) claim to have demonstrated an ability to discrim-
inate between AD patients and healthy controls with high
degrees of sensitivity and specificity [6–11]. However, the
theoretical bases of and ubiquitous patterns in the reported
data raise a host of lingering questions that should be
addressed before these radiotracers are clinically approved.
Anomalies in the distribution of amyloid radiotracers
One of the more troubling aspects of amyloid imaging is
the striking discrepancy in the distribution of Aβ deposits
in the brain between PET images produced with amyloid
tracers and histopathological and immunohistochemical
studies, which should be—and have been—held up as the
reference standard. A phase III study of florbetapir by Clark
Eur J Nucl Med Mol Imaging (2012) 39:202–208
M.C. Moghbel and B. Saboury are co-first authors.
M. C. Moghbel:B. Saboury:S. D. Metzler:D. A. Torigian:
A. Alavi (*)
Department of Radiology, Hospital of University of Pennsylvania,
3400 Spruce Street,
Philadelphia, PA 19104, USA
M. C. Moghbel
Radiation Medicine Center (BARC), Tata Memorial Hospital,
Department of Biochemistry and Organic Chemistry,
Department. of Nuclear Medicine,
University of Southern Denmark,
Center for Pharmacology and Therapeutics,
Neuropsychopharmacology Unit, Imperial College,
et al. reported significant correlations between quantitative
and semiquantitative measures of overall amyloid burden
through imaging and histopathology, but the regional
localizations of these amyloid deposits do not seem to
agree with pre-existing pathological data . Imaging
studies conducted with florbetapir, as well as virtually any
other amyloid radiotracer, consistently show the frontal
lobe to have one of—if not the—highest standardized
uptake values (SUVs) [6, 8, 9, 11–14]. This implies a
preponderant amyloid burden in the frontal lobe, which is
not in line with the findings of in vitro studies.
By contrast, a comprehensive histopathological survey
of the cortices found the highest density of neuritic plaques
in the temporal and occipital lobes, an intermediate
accumulation in the parietal lobe, and the lowest concentration
in the limbic and frontal lobes  (Fig. 1). A similar
pathological study of 2,661 autopsy cases found that amyloid
plaques are concentrated in the temporal gray matter and the
perirhinal and ectorhinal fields in early AD, and do not spread
to the frontal lobe until later stages of the disease . A
study of cerebral degeneration, which is considered by the
amyloid hypothesis to be a direct consequence of Aβ
deposition, pinpointed the medial temporal cortex as the
epicenter of neuronal deterioration, with damage radiating
first and foremost to the parietal and occipital areas. In this
cascade of neurodegeneration, the frontal lobe is shown to
occupy only an intermediate position .
This point has been well illustrated in scans conducted
using other imaging modalities, as well as PET with non-
amyloid tracers. Magnetic resonance imaging (MRI) scans of
AD patients reveal the greatest degree of atrophy in the
temporal and parietal lobes, with frontal lobe damage delayed
until the late stages of the disease [18–20]. Likewise,
functional MRI studies have shown that the most pro-
nounced differences in blood flow between AD patients and
controls occur in the temporal and parietal lobes . Even
PET, when measuring the glucose metabolism of the brain
with [18F]FDG rather than its amyloid burden, exhibits a
similar pattern of hypometabolism in the parietotemporal
region, where over 50% of metabolic reductions occur [22–
24]. With this compelling and diverse set of evidence in
mind, the preferential uptake in the frontal lobes that is
invariably exhibited by amyloid imaging agents raises
questions about the specificity of these radiotracers. These
issues are further underscored as the phase III florbetapir
study showed that the frontal lobe has one of the lowest
correlations between measures of amyloid burden through in
vivo imaging and at autopsy through histopathology .
binding by the amyloid agents, but there may be an alternative
explanation: the distribution of cerebral amyloid angiopathy
(CAA), a condition involving an accumulation of Aβ in the
vasculature of the brain, is distinct from that of AD. CAA is
most frequently associated with the arteries of the frontal and
occipital lobes [25, 26]. It is also far more common in AD
patients, with reported comorbidity rates between 82% and
87%, than in non-AD individuals, in whom incidence rates
between 26% and 30% have reported [27–29]. Thus, the
prevalence of CAA in AD patients could account not only for
the observation that uptake of amyloid agents in the frontal
lobes is unexpectedly one of the highest, but also that the
difference in uptake between AD patients and controls
is most pronounced in the frontal lobe .
However, CAA cannot explain another anomaly in
amyloid imaging: the substantial uptake of radiotracers in
the white matter of the brain, which is believed to be nearly
devoid of Aβ plaques. PET images produced with amyloid
agents consistently show higher ratios of white matter to
Fig. 1 Neuroanatomical distribution of neuritic plaques in the brain of
AD patients, as revealed by histopathological and immunohistochem-
ical staining. The amyloid burdens are rated on an arbitrary scale of 0
(lowest) to 4 (highest). The typical histopathologically observed
pattern of comparatively high concentrations of amyloid in the
temporoparietal lobes and the low concentrations in the frontal lobe
should be noted and contrasted against the contradictory distribution
pattern captured by amyloid imaging (reproduced with permission
from Cerebral Cortex, )
Eur J Nucl Med Mol Imaging (2012) 39:202–208 203
gray matter uptake than immunohistochemical tests .
This pattern of white matter uptake of amyloid radiotracers
has been largely described as a product of non-specific
binding, but has also been speculated to be an artifact of a
slower clearance rate due to lower blood flow in the white
matter than in the gray matter [30, 31]. The proposed
dependence of tracer concentration on blood flow is
supported by the observed relationship between cerebral
blood flow and influx of [11C]PiB into the brain .
Furthermore, it has been postulated that high radiotracer
uptake in the gray matter of AD patients may spill over into
the white matter, thereby artificially inflating SUVs .
This may not be able to explain the inordinately high
uptake in the white matter of control subjects, and in fact
raises the possibility of uptake in the white matter of
controls spilling over into the gray matter (Fig. 2). This
appears exceedingly likely considering the susceptibility of
structures as small as amyloid plaques to the partial-volume
effect, which is seen in most imaging modalities, including
Difficulties in visualizing amyloid plaques
The relatively poor spatial resolution of PET is associated
with the phenomenon of the partial-volume effect, which
results in the underestimation of a structure’s SUV. This
effect is especially severe for structures that are less than
2.5 times the spatial resolution of the PET system, as
measured by the full-width at half-maximum . Since
amyloid plaques are, on average, approximately 50 µm in
diameter and the spatial resolution of PET is normally in
the range of 2 to 3 mm for high-resolution systems and 5 to
7 mm for standard scans, the partial-volume effect should
theoretically be factored heavily into amyloid imaging and
should not be overlooked [36, 37]. And yet Wong et al.
explicitly state that in a phase I clinical study of florbetapir
partial-volume correction was not undertaken in the
analysis of data . The reports of other studies on amyloid
radiotracers make no mention of partial-volume correction,
suggesting that this highly significant effect was also
neglected in these studies. The result of this would be a
prevalence of underestimated SUV data in the literature.
Perhaps the most fundamental question about amyloid
imaging concerns the ability of PET to visualize Aβ
deposits. In area 9 of the frontal cortex—a region
demonstrating remarkably high uptake of amyloid radio-
tracers—the percentage of total area occupied by amyloid
plaques was shown by one histopathological study to be
approximately 7.11% in AD patients . Similarly, the
histopathological component of the phase III florbetapir
study found that the average amyloid burden in the
precuneus, another region of high uptake, was 5.24% in
nine patients for whom AD was named as the cause of
death . If it is assumed that amyloid burden in end-stage
AD constitutes roughly 6% (in terms of area fraction) of the
most severely affected cortical regions, and that the contrast
between plaque and background must be at least twofold in
order to visualize these structures with PET, the differential
uptake of these amyloid tracers must be at least 100 times
greater in amyloid plaques than in the background
(Appendix; Fig. 3). This estimate would be considerably
higher in regions with less amyloid burden, as well as in
patients with less advanced AD, in whom amyloid imaging
would presumably be most diagnostically valuable. For
example, in patients with mild cognitive impairment (MCI)/
prodromal AD, in whom the amyloid burden would be
closer to 0.1% of the area involved (equivalent to 0.03% of
the total volume involved), the differential affinity for
amyloid would have to be at least 6,000 times that of the
background. This is a tall order for any radiotracer, but
seems especially unlikely for amyloid imaging agents
considering the extent of their non-specific uptake in the
white matter, as well as other regions in the gray matter.
Binding properties of amyloid plaques
The conspicuous discrepancies between in vivo imaging
and in vitro testing in the white matter and frontal lobe may
be partly attributable to the inherent difficulties of targeting
fibrillar amyloid plaques, which are not as well-defined as
the soluble forms of the protein. Studies have shown that
Fig. 2 Uptake in the white matter of the brain spilling over into the
gray matter, and vice versa, using typical uptake profiles for PET
imaging . The black circles represent increasing proportions of
cortical area occupied by Aβ, while the blue lines signify the uptake
profiles at these respective amyloid burdens. The red line illustrates
the substantial radiotracer uptake observed in the white matter, which
will unavoidably overwhelm the signal from the gray matter at mild to
moderate amyloid burdens
204Eur J Nucl Med Mol Imaging (2012) 39:202–208
despite their ability to bind to Aβ in fibrillar plaques and
cerebral arteries, amyloid radiotracers such as [11C]PiB
have a low affinity for amorphous plaques in the cortices
. The possibility that Aβ precipitates differently
according to the region of the brain and the stage of the
disease introduces the problem of disparate surface struc-
tures. This would pose a major challenge to the specificity
of amyloid imaging agents, and should therefore be ruled
out by performing in vivo dose-binding studies.
Autoradiographical binding studies have been performed
on amyloid radiotracers to establish their specificity, but
certain methodological oversights necessitate further in
vivo testing to validate their conclusions . A key
contributor to the uncertainty surrounding binding specific-
ity is the fact that while the development of imaging agents
such as [11C]PiB accounted for the microdoses that would
ultimately reach the targeted proteins in vivo, binding
studies were performed ex vivo in a medium that had an
excess of the radiotracer [31, 40, 42]. This difference in
dosage serves as a confounding variable that hinders the
conclusiveness of the binding studies that have been
performed thus far.
Theoretical basis of amyloid imaging
In addition to these practical concerns regarding amyloid
radiotracers, there is the broader issue of the theoretical
underpinnings of amyloid imaging. The ability of this
technique to diagnose early AD rests upon the assumption
that Aβ plays an etiological role in the progression of the
disease, for the density of a non-etiological biomarker
would not necessarily be correlated with cognitive decline.
This is precisely what was found by Bennett et al. in the
frontal cortex of AD patients, where amyloid plaque burden
did not reflect dementia severity . This raises the point
that while the correlation between Aβ and AD is well
Fig. 3 The relationship between
the contrast (C) in a PET image
and the differential uptake ratio
(D) of radiotracer uptake in
targeted β-amyloid plaques to
the background is represented
linearly at three distinct stages
of disease progression, where
C=fv(D−1) (see Appendix for
details). Patients with MCI,
early AD, and late AD are
assumed to have roughly 0.1%,
1%, and 6% of total cortical area
(i.e., area fractions fa) occupied
by β-amyloid, respectively,
and corresponding volume frac-
tions (fv) of roughly 0.03%,
0.33%, and 2.0%, respectively
Fig. 4 A scatterplot of mean cortical SUV ratios in individuals with
probable AD (PAD) and mild cognitive impairment (MCI), as well as
older healthy controls (OHC) . Noteworthy false-negative and
false-positive rates can be seen in AD patients and controls,
respectively (reproduced with permission from Archives of Neurology,
Eur J Nucl Med Mol Imaging (2012) 39:202–208 205
established, the claim of causation is still a matter of debate
and has recently been challenged by developments in the
clinical trials of antiamyloid pharmaceuticals.
Drugs and vaccines that reached the latter stages of FDA
testing, such as tramiprosate, tarenflurbil, bapineuzumab,
semagacestat, and AN-1792, often proved exceptionally
effective at reducing Aβ burden in the brain, but ultimately
failed to demonstrate a significant slowdown in cognitive
decline when compared to controls [44–52]. Furthermore,
the literature reveals that 10–20% of patients with clinically
diagnosed AD do not have amyloid pathology at autopsy,
and that 15–20% of Aβ-positive PET scans are seen in
subjects with no cognitive deficits. This is consistent with
reports that significant Aβ deposits can be found in the
brain of cognitively normal elderly individuals at autopsy
. A possible implication of these findings is that Aβ is
not the direct cause of the synaptic and neuronal injury that
triggers AD, but is rather part of a larger cascade that
dictates the disease process.
of false-positive and false-negative PET scans using amyloid
tracers[53, 54]. Amyloid imaging with [11C]PiB has revealed
that over one-fifth of cognitively unimpaired individuals over
the age of 65 years demonstrate uptake above a predeter-
mined threshold for AD in at least one region of interest .
On the other hand, case studies in the literature reveal that
subjects with declining cognitive function, positive tests for
AD biomarkers in the cerebrospinal fluid, and positive
pathological tests at autopsy can still demonstrate [11C]PiB
binding below detectable levels . This was reflected in a
recent study using florbetapir, which showed significant
overlap between scans of patients with probable AD, patients
with MCI, and older healthy controls (Fig. 4) . These
observations severely limit the utility of this PET imaging
approach for accurately diagnosing the presence or absence
of MCI, let alone early AD.
Based on the extent of amyloid deposition in cognitively
normal individuals and the inexplicably high degree of
activity detected in the frontal lobe and white matter, it would
appear that the claims made about the high degrees of
sensitivity and specificity of amyloid imaging agents in
detecting AD are not justifiable. When the pathologically
established distribution of Aβ is taken into account, the
probability of extensive nonspecific uptake by biological
processes appears inescapably high. It is of paramount
importance that such fundamental theoretical and practical
concerns be thoroughly investigated and properly addressed
before amyloid tracers enter the market en masse and expand
the applications of amyloid imaging to the clinical field.
Appendix: Converting area fractions to volume fractions,
and calculating the contrast produced by various
differential uptake ratios and amyloid burdens
The findings of the cited histopathological and immuno-
histochemical studies with data on the amyloid burden of
AD patients are expressed as ratios of two-dimensional
(2D) cross-sectional areas (fa). However, since the voxels
that comprise a PET image are three-dimensional cubes,
these values have to be recalculated for three-dimensional
volumes (fv). Assuming that (1) the plaques are spherical
with radius R, (2) the plaques are either completely or not at
all within the 2D slice of thickness T and area of interest A,
and (3) that he 2D measurement accurately represents the
widest cross section of the (spherical) plaque, the problem
reduces to finding the number of plaques in the slice:
N ¼ Afa= pR2
like fa. In this calculation, we take the average radius of the
sphere to be 25 µm .
Now assume that a voxel with no amyloid activity (i.e.,
background only) has unit concentration. Then a voxel on
PET imaging containing amyloid activity can be segmented
into the volume fraction for amyloid plaque (fv), which has
differential uptake ratio D, and the volume fraction without
plaque (1−fv), which has unit background activity. The
total activity concentration (i.e., signal) for this voxel is fvD+
(1−fv). Therefore, the contrast C is given by (signal−
ðÞ. The volume fraction is then fv¼ 4=3
Þ ¼ 4=3
ðÞfa, which is dimensionless, just
1. Brookmeyer R, Johnson E, Ziegler-Graham K, Arrighi HM.
Forecasting the global burden of Alzheimer’s disease. Alzheimers
2. McKhann G, Drachman D, Folstein M, Katzman R, Price D,
Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of
the NINCDS-ADRDA Work Group under the auspices of
Department of Health and Human Services Task Force on
Alzheimer’s disease. Neurology. 1984;34(7):939–44.
3. Dubois B, Feldman HH, Jacova C, Dekosky ST, Barberger-
Gateau P, Cummings J, et al. Research criteria for the diagnosis of
Alzheimer’s disease: revising the NINCDS-ADRDA criteria.
Lancet Neurol. 2007;6(8):734–46.
4. Khachaturian ZS. Diagnosis of Alzheimer’s disease. Arch Neurol.
5. Hardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer’s
disease: progress and problems on the road to therapeutics.
6. Wong DF, Rosenberg PB, Zhou Y, Kumar A, Raymont V, Ravert
HT, et al. In vivo imaging of amyloid deposition in Alzheimer
disease using the radioligand 18F-AV-45 (Florbetapir F18). J Nucl
206Eur J Nucl Med Mol Imaging (2012) 39:202–208
7. Clark CM, Schneider JA, Bedell BJ, Beach TG, Bilker WB,
Mintun MA, et al. Use of florbetapir-PET for imaging β-amyloid
pathology. JAMA. 2011;305(3):275–83.
8. Fleisher AS, Chen K, Liu X, Roontiva A, Thiyyagura P,
Ayutyanont N, et al. Using positron emission tomography and
florbetapir F18 to image cortical amyloid in patients with mild
cognitive impairment or dementia due to Alzheimer disease. Arch
Neurol. 2011. http://archneur.ama-assn.org/cgi/content/full/
archneurol.2011.150v1. Accessed 4 October 2011.
9. Barthel H, Gertz HJ, Dresel S, Peters O, Bartenstein P, Buerger K,
et al. Cerebral amyloid-β PET with florbetaben (18F) in
patients with Alzheimer’s disease and healthy controls: a
multicenter phase 2 diagnostic study. Lancet Neurol. 2011;10
10. Villemagne VL, Ong K, Mulligan RS, Holl G, Pejoska S, Jones G,
et al. Amyloid imaging with (18)F-florbetaben in Alzheimer
disease and other dementias. J Nucl Med. 2011;52(8):1210–7.
11. Vandenberghe R, Van Laere K, Ivanoiu A, Salmon E, Bastin C,
Triau E, et al. 18F-Flutemetamol amyloid imaging in Alzheimer
disease and mild cognitive impairment: a phase 2 trial. Ann
12. Barthel H, Luthardt J, Becker G, Patt M, Hammerstein E, Hartwig
K, et al. Individualized quantification of brain β-amyloid burden:
results of a proof of mechanism phase 0 florbetaben PET trial in
patients with Alzheimer's disease and healthy controls. Eur J Nucl
Med Mol Imaging. 2011;38(9):1702–14.
13. Kemppainen NM, Aalto S, Wilson IA, Nagren K, Helin S, Bruck
A, et al. PET amyloid ligand [11C]PIB uptake is increased in mild
cognitive impairment. Neurology. 2007;68(19):1603–6.
14. Kemppainen NM, Aalto S, Wilson IA, Nagren K, Helin S, Bruck
A, et al. Voxel-based analysis of PET amyloid ligand [11C]PIB
uptake in Alzheimer disease. Neurology. 2006;67(9):1575–80.
15. Arnold SE, Hyman BT, Flory J, Damasio AR, Van Hoesen GW.
The topographical and neuroanatomical distribution of neurofibril-
Alzheimer’s disease. Cereb Cortex. 1991;1(1):103–16.
16. Braak H, Braak E. Frequency of stages of Alzheimer-related
lesions in different age categories. Neurobiol Aging. 1997;18
17. Brun A, Gustafson L. Distribution of cerebral degeneration of
Alzheimer’s disease. Arch Psychiatr Nervenkr. 1976;223(1):15–
18. Shin J, Kepe V, Small GW, Phelps ME, Barrio JR. Multimodal
imaging of Alzheimer pathophysiology in the brain’s default
mode network. Int J Alzheimers Dis. 2011:687945.
19. Scahill RI, Schott JM, Stevens JM, Rossor MN, Fox NC.
Mapping the evolution of regional atrophy in Alzheimer’s disease:
unbiased analysis of fluid-registered serial MRI. Proc Natl Acad
Sci U S A. 2002;99(7):4703–7.
20. Thompson PM, Hayashi KM, de Zubicaray G, Janke AL, Rose
SE, Semple J, et al. Dynamics of gray matter loss in Alzheimer’s
disease. J Neurosci. 2003;23(3):994–1005.
21. Yetkin FZ, Rosenberg RN, Weiner MF, Purdy PD, Cullum CM.
FMRI of working memory in patients with mild cognitive
impairment and probable Alzheimer’s disease. Eur Radiol.
22. Mosconi L, Tsui WH, Herholz K, Pupi A, Drzezga A, Lucignani
G, et al. Multicenter standardized 18F-FDG PET diagnosis of
mild cognitive impairment, Alzheimer’s disease, and other
dementias. J Nucl Med. 2008;49(3):390–8.
23. Herholz K, Salmon E, Perani DBaron JC, Holthoff V, Frolich L, et
al. Discrimination between Alzheimer dementia and controls by
automated analysis of multicenter FDG PET. Neuroimage.
24. Furst AJ, Lal RA. Amyloid-B and Glucose Metabolism in
Alzheimer’s Disease. J Alzheimers Dis. 2011;26:105–16.
25. Attems J, Quass M, Jellinger KA, Lintner F. Topographical
distribution of cerebral amyloid angiopathy and its effect on
cognitive decline are influenced by Alzheimer disease pathology.
J Neurol Sci. 2007;257(1–2):49–55.
26. Xu D, Yang C, Wang L. Cerebral amyloid angiopathy in aged
Chinese: a clinic-neuropathological study. Acta Neuropathol.
27. Ti M. The incidence of cerebral amyloid angiopathy in
Alzheimer’s disease. Neurology. 1975;25(2):120–6.
28. Ellis RJ, Olichney JM, Thal LJ, Mirra SS, Morris JC, Beekly D, et
al. Cerebral amyloid angiopathy in the brains of patients with
Alzheimer’s disease. Neurology. 1996;46(6):1592–6.
29. Esiri MM, Wilcock GK. Cerebral amyloid angiopathy in
dementia and old age. J Neurol Neurosurg Psychiatry. 1986;49
30. Fodero-Tavoletti MT, Rowe CC, McLean CA, Leone L, Li QX,
Masters CL, et al. Characterization of PiB binding to white matter
in Alzheimer disease and other dementias. J Nucl Med. 2009;50
31. Klunk WE, Engler H, Nordberg A, Wang Y, Blomqvist G, Holt
DP, et al. Imaging brain amyloid in Alzheimer’s disease with
Pittsburgh Compound-B. Ann Neurol. 2004;55(3):306–19.
32. Blomquist G, Engler H, Nordberg A, Ringheim A, Wall A,
Forsberg A, et al. Unidirectional influx and net accumulation of
PIB. Open Neuroimaging J. 2008;2:114–25.
33. Thomas BA, Erlandsson K, Modat M, Thurfjell L, Vandenberghe
R, Ourselin S, et al. The importance of appropriate partial volume
correction for PET quantification in Alzheimer’s disease. Eur J
Nucl Med Mol Imaging. 2011;38(6):1104–19.
34. Hickeson M, Yun M, Matthies A, Zhuang H, Adam LE, Lacorte
L, et al. Use of corrected standardized uptake value based on the
lesion size on CT permits accurate characterization of lung
nodules on FDG-PET. Eur J Nucl Med Mol Imaging. 2002;29
35. Kessler RM, Ellis JR, Eden M. Analysis of emission tomographic
scan data: limitations imposed by resolution and background. J
Comput Assist Tomogr. 1984;8(3):514–22.
36. Connor DM, Benveniste H, Dilmanian FA, Kritzer MF, Miller
LM, Zhong Z. Computed tomography of amyloid plaques in a
mouse model of Alzheimer’s disease using diffraction enhanced
imaging. Neuroimage. 2009;46(4):908–14.
37. Sanchez-Crespo A, Andreo P, Larsson SA. Positron flight in
human tissues and its influence on PET image spatial resolution.
Eur J Nucl Med Mol Imaging. 2004;31(1):44–51.
38. Bussiere T, Friend PD, Sadeghi N, Wicinski B, Lin GI, Bouras C,
et al. Stereologic assessment of the total cortical volume occupied
by amyloid deposits and its relationship with cognitive status in
aging and Alzheimer’s disease. Neuroscience. 2002;112(1):75–91.
39. Cairns NJ, Ikonomovic MD, Benzinger T, Storandt M, Fagan AM,
Shah AR, et al. Absence of Pittsburgh compound B detection of
cerebral amyloid beta in patient with Alzheimer disease: a case
report. Arch Neurol. 2009;66(12):1557–62.
40. Lin KJ, Hsu WC, Hsiao IT, Wey SP, Jin LW, Skovronsky D, et al.
Whole-body biodistribution and brain PET imaging with [18F]
AV-45, a novel amyloid imaging agent – a pilot study. Nucl Med
41. Bergström M, Grahnén A, Långström B. Positron emission
tomography microdosing: a new concept with application in
tracer and early clinical drug development. Eur J Clin Pharmacol.
42. Lister-James J, Pontecorvo MJ, Clark C, Joshi AD, Mintun MA,
Zhang W, et al. Florbetapir F-18: a histopathologically validated
beta-amyloid positron emission tomography imaging agent. Semin
Nucl Med. 2011;41(4):300–4.
43. Bennett DA, Cochran EJ, Saper CB, Leverenz JB, Gilley DW,
Wilson RS. Pathological changes in frontal cortex from biopsy to
Eur J Nucl Med Mol Imaging (2012) 39:202–208207
autopsy in Alzheimer’s disease. Neurobiol Aging. 1993;14 Download full-text
44. Aisen PS, Gauthier S, Ferris SH, Saumier D, Haine D, Garceau D,
et al. Tramiprosate in mild-to-moderate Alzheimer’s disease – a
randomized, double-blind, placebo-controlled, multi-centre study
(the Alphase Study). Arch Med Sci. 2011;7(1):102–11.
45. Green RC, Schneider LS, Amato DA, Beelen AP, Wilcock G,
Swabb EA, et al. Effect of tarenflurbil on cognitive decline and
activities of daily living in patients with mild Alzheimer disease: a
randomized control trial. JAMA. 2009;302(23):2557–64.
46. Salloway S, Sperling R, Gilman S, Fox NC, Blennow K, Raskind
M, et al. A phase 2 multiple ascending dose trial of bapineuzumab
in mild to moderate Alzheimer disease. Neurology. 2009;73
47. Panza F, Frisardi V, Imbimbo BP, D’Onofrio G, Pietrarossa G,
Seripa D, et al. Bapineuzumab: anti-β-amyloid monoclonal
antibodies for the treatment of Alzheimer’s disease. Immunother-
48. Imbimbo BP, Panza F, Frisardi V, Solfrizzi V, D’Onofrio G,
Logroscino G, et al. Therapeutic intervention for Alzheimer's
disease with γ-secretase inhibitors: still a viable option?. Expert
Opin Investig Drugs. 2011;20(3):325–41.
49. Robinson SR, Bishop GM, Lee HG, Munch G. Lessons from the
AN 1792 Alzheimer vaccine: lest we forget. Neurobiol Aging.
50. Munch G, Robinson SR. Alzheimer’s vaccine: a cure as
dangerous as the disease? J Neural Transm. 2002;109(4):537–9.
51. Rafii MS, Aisen PS. Recent developments in Alzheimer’s disease
therapeutics. BMC Med. 2009;7:7.
52. Sabbagh MN. Drug development for Alzheimer’s disease: where
are we now and where are we headed?. Am J Geriatr Pharmacother.
53. Mayeux R, Saunders AM, Shea S, Mirra S, Evans D, Roses AD,
et al. Utility of the apolipoprotein E genotype in the diagnosis of
Alzheimer's disease. Alzheimer's Disease Centers Consortium on
Apolipoprotein E and Alzheimer's Disease. N Engl J Med.
54. Ranginwala NA, Hynan LS, Weiner MF, White III CL. Clinical
criteria for the diagnosis of Alzheimer disease: still good after all
these years. Am J Geriatr Psychiatry. 2008;18(5):384–8.
55. Aizenstein HJ, Nebes RD, Saxton JA, Price JC, Mathis CA,
Tsopelas ND, et al. Frequent amyloid deposition without
significant cognitive impairment among the elderly. Arch Neurol.
208Eur J Nucl Med Mol Imaging (2012) 39:202–208