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j.neuroimage.2018.08.044

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Accepted Manuscript
Implementing the centiloid transformation for
using CapAIBL
11
C-PiB and β-amyloid
18
F-PET tracers
Pierrick Bourgeat, Vincent Doré, Jurgen Fripp, David Ames, Colin L. Masters, Olivier
Salvado, Victor L. Villemagne, Christopher C. Rowe
PII:
S1053-8119(18)30742-0
DOI:
10.1016/j.neuroimage.2018.08.044
Reference:
YNIMG 15205
To appear in:
NeuroImage
Received Date: 7 March 2018
Revised Date:
9 July 2018
Accepted Date: 17 August 2018
Please cite this article as: Bourgeat, P., Doré, V., Fripp, J., Ames, D., Masters, C.L., Salvado, O.,
Villemagne, V.L., Rowe, C.C., the AIBL research group, Implementing the centiloid transformation
11
18
for C-PiB and β-amyloid F-PET tracers using CapAIBL, NeuroImage (2018), doi: 10.1016/
j.neuroimage.2018.08.044.
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Implementing the centiloid transformation for 11C-PiB and β-amyloid 18FPET tracers using CapAIBL
Pierrick Bourgeat1, Vincent Doré1,2, Jurgen Fripp1, David Ames3, Colin L. Masters4, Olivier Salvado1,
1. CSIRO Health and Biosecurity, Brisbane, Australia
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Victor L. Villemagne2,4,5, Christopher C. Rowe2,5 and the AIBL research group
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2. Department of Molecular Imaging, Austin Health, Melbourne, Australia
3. National Ageing Research Institute, Parkville, Victoria, Australia.
Victoria, Australia.
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4. The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville,
5. Department of Medicine, University of Melbourne, Melbourne, Australia
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Corresponding author: Pierrick Bourgeat
Level 5 UQ Health Sciences Building, Royal Brisbane and Women's Hospital, Herston, Queensland
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4029 Australia.
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Tel:(+61)732533659
Email:Pierrick.Bourgeat@csiro.au
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Abstract The centiloid scale was recently proposed to provide a standard framework for the
quantification of β-amyloid PET images, so that amyloid burden can be expressed on a standard
scale. While the framework prescribes SPM8 as the standard analysis method for PET
quantification, non-standard methods can be calibrated to produce centiloid values. We have
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previously developed a PET-only quantification: CapAIBL. In this study, we show how CapAIBL can
be calibrated to the centiloid scale.
Methods Calibration images for 11C-PiB, 18F-NAV4694, 18F-Florbetaben, 18F-Flutemetamol and 18F-
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Florbetapir were analysed using the standard method and CapAIBL. Using these images, both
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methods were calibrated to the centiloid scale. Centiloid values computed using CapAIBL were
compared to those computed using standard method. For each tracer, a separate validation was
performed using an independent dataset from the AIBL study.
Results Using the calibration images, there was a very strong agreement, and very little bias
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between the centiloid values computed using CapAIBL and those computed using the standard
method with R2 > 0.97 across all tracers. Using images from AIBL, the agreement was also high
with R2 > 0.96 across all tracers. In this dataset, there was a small underestimation of the centiloid
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values computed using CapAIBL of less than 0.8% in PiB, and a small over-estimation of 1.3% in
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Florbetapir, and 0.8% in Flutemetamol. There was a larger overestimation of 8% in NAV images,
and 14% underestimation in Florbetaben images. However, some of these differences could be
explained by the use of different scanners between the calibration scans and the ones used in
AIBL.
Conclusion The PET-only quantification method, CapAIBL, can produce reliable centiloid values.
The bias observed in the AIBL dataset for 18F-NAV4694 and
18
F-Florbetaben may indicate that
using different scanners or reconstruction methods might require scanner-specific adjustments.
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Keywords: Alzheimer's disease; Amyloid Imaging; Centiloid
1. Introduction
β-amyloid (Aβ) imaging using PET has become the de facto standard to identify one of the
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neuropathological signs of Alzheimer’s disease (AD) in vivo. There is however great variability in
the way Aβ burden is quantified from PET images, with each research centre developing their own
pipeline to analyse the images, and using their own volume of interest (VOI) to compute the mean
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uptake in the neocortical and reference regions. This leads to great variability in the resulting
numbers (1), and makes comparison across sites or studies difficult (2). This also means that
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pooling data across studies requires all images to be reanalysed to minimise bias. The variability is
also exacerbated by the use of different tracers, each having different pharmacokinetics, binding
potentials, recommended acquisition protocol and reference region (3,4,5,6). There are currently
5 different tracers that are commonly used in research studies, namely 11C-PiB (PiB), 18F- NAV4694
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(NAV), 18F-Florbetaben (FBB), 18F-Flutemetamol (FLUTE) and 18F-Florbetapir (FBP), with each tracer
having their own recommended acquisition protocol, reference region and cut-off value for Aβ
positivity. However, due to the variability from the quantification method, these cut-off values
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often need to be adjusted at each site so that they match visual reading.
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The centiloid (CL) scale was developed by an international working group to alleviate some of
these issues and provide a framework to standardise measures of Aβ burden from PET images (7).
The framework allows any tracer or quantification method to be linearly mapped to the same
scale: the centiloid scale. In this scale, 0 represents the typical Aβ burden in young controls, and
100 the typical Aβ burden in mild AD patients. A standard quantification pipeline based on SPM8 is
prescribed to perform the initial anchoring of the centiloid scale, and calibration of new tracers or
methods. To promote wide adoption of the centiloid scale, the VOI masks required for the
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quantification and the images needed for the calibration are all made freely available on the
GAAIN website. As part of this effort, the calibration images and associated equations have
already been released for 11C-PiB (7), 18F- NAV4694 (8), 18F-Florbetaben (9), 18F-Flutemetamol (10)
and 18F- Florbetapir (11).
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One of the limitation of the standard SPM8 pipeline is that each PET image requires a
corresponding MR image to provide anatomical constrain during the spatial normalisation to the
common template. This can be an issue when MR imaging is impossible (claustrophobia, metal
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implants, non-compliant subject…), as this would prevent the automatic quantification of the
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accompanying PET image. For this reason, we have developed a PET-only approach
(Computational Analysis of PET from AIBL or CapAIBL for short) that provide SUVR quantification
of Aβ PET images without the need of a corresponding MR image (12,13). In this study, we apply
the centiloid framework to the calibration of CapAIBL, and compare centiloid value derived from
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2. Materials and Methods
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all 5 tracers to those computed using the standard method.
2.1. Standard SPM pipeline
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Each subject’s MRI was spatially normalised to the MNI-152 template using the SPM8 unified
segmentation method. Each subject’s PET was then co-registered to their corresponding MRI, and
spatially normalised to the template using the deformation parameters calculated from the MRI.
For each subject, the mean retention within the standard VOIs from the GAAIN website, namely
global cortical target (CTX) VOI and whole cerebellum (WC) VOI, was computed. The CTX/WC ratio
was used to define the subject’s standardised uptake value ratio (SUVR). Since the whole
cerebellum was found to be the reference region that gave the smallest standard deviation in both
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AD and young controls (YC), and lead to the largest effect size (7), this is the only reference region
that will be considered in this study, and every mention of the SUVR will refer to the neocortical
SUVR normalised by the whole cerebellum.
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2.2. PET-only SUVR quantification using CapAIBL
When no MR is available, PET images can be spatially normalising to a mean PET template directly,
as is commonly done with FDG. However, when performed using Aβ PET images, the spatial
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normalisation introduces a bias in the resulting quantification, with an overestimation of SUVR in
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Aβ negative subjects, and an under-estimation in Aβ positive subjects (14). This effect can be
reduced by using an adaptive template that matches its retention to that of the subject’s PET (12).
The adaptive template is a linear combination of an Aβ negative (Aneg) and Aβ positive (Apos)
template, with a weight w optimised by maximising the normalised mutual information between
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the adaptive template and the target:
A( w) = w * Aneg + (1 − w) * Apos
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The adaptive template is optimised for each PET image after the affine registration of the PET to
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the template. After optimisation, the spatial normalisation to the template can be performed
directly without the need for an MRI. The subsequent quantification steps are then identical to
those performed as part of the standard SPM8 pipeline.
2.3. Level-1 centiloid replication analysis
The first step is to check that we can replicate the results of the Level-1 centiloid calibration using
the SPM8 standard pipeline. In order to improve clarity, the notations used in this manuscript
might differ slightly from the ones employed in the original centiloid paper (7).
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The 50-70 minutes PiB PET images and corresponding MRI from the 34 YC, and 45 AD were
downloaded from the GAAIN website. All images were then processed using the recommended
SPM8 pipeline. SUVR values for each scan were computed using the standard cortex mask, and
whole cerebellum as the reference region. Centiloid (CL) values were computed using the
∗
is the mean SUVR of the 34 YC,
∗
AD, and
∗
−
∗
∗
∗
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Where
−
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∗
= 100 ×
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following equation:
is the mean SUVR of the 45
is the SUVR of an individual subject, with the asterisk denoting that the SUVR
were computed at our site (using the standard SPM8 pipeline). This gave us the following equation:
− 1.014
Equation1
1.073
∗
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= 100 ×
We then performed a linear correlation of our centiloid values, to the ones reported in (7). The R2
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was 0.999 and the intercept was -0.073 CL, both within the accepted range (R2>0.98 and intercept
between −2 and 2 CL). For the remainder of this manuscript, the centiloid values computed using
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the SPM8 pipeline at our site, as well as the centiloid equation defined in Eq 1, will be used in all
calculations.
2.4. Level-2 Calibration of CapAIBL using PiB
In order to perform centiloid quantification using CapAIBL, the SPM template used in the standard
pipeline was spatially normalised to the adaptive template used in CapAIBL. The centiloid VOIs
were then propagated accordingly to the adaptive template’s space.
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The 50-70 minutes PiB PET images from the 34 YC and 45 AD were spatially normalised to the
adaptive template using CapAIBL, and their SUVR were computed using the centiloid VOIs.
Calibrating a new method is a 2 steps process, where the PiB SUVR computed using the nonstandard method is first transformed into PiB-calculated SUVR or PiB-calcSUVR (as referred to in (7)),
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which are equivalent to the PiB SUVR computed using the standard SPM8 pipeline. These
calc
SUVR can then be transformed into centiloid using Eq 1.
The first step is to compute the slope (PiBmCapAIBL) and Intercept (PiBbCapAIBL) of the SUVR
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i.
PiB-
'( ) * ~
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computed using CapAIBL against those computed using the standard centiloid, so that
,
'( ) * ×
∗
+
.
'( ) *
Since the tracer is kept the same, but the quantification methods are different, the
parameters (PiBmCapAIBL,PiBbCapAIBL) inform on the differences due to the use of CapAIBL. After
PiB
SUVRCapAIBL is converted into
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checking that the R2 is above 0.7,
PiB-calc
SUVRCapAIBL using the
standard method using the calculated slope and intercept, so that:
PiB-calc
'( ) *
−
,
.
'( ) * '( ) *
SUVRCapAIBL is then converted into centiloid using Eq 1.
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ii.
'( ) * = EP
/'0/
Both equations are combined to directly transform PiBSUVRCapAIBL into centiloid.
2.5. Level-2 Calibration of the 18F tracers using the standard method
In this section, we will run the level-2 calibration of each
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F tracer into centiloid using the
standard SPM8 pipeline. For each tracer, its PET images and corresponding PiB PET and MR images
were downloaded from the GAAIN website. Static images were available for all tracers except for
the
18
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F-Florebetapir-11C-PiB dataset where dynamic scans are the only images available. For this
dataset, the individual frames were summed together. No frame-to-frame motion correction was
applied, and no other pre-processing was done on the data. Basic demographics are provided in
Table 1.
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Table 1. Demographics
N
Clinical group
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79
34 YC, 45 AD
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55
10 YC, 25 HC, 10 MCI, 7 AD, 3 FTD
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35
10 YC, 6 HC, 9 MCI, 8 AD, 2 FTD
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74
24 YC, 10 HC, 20 MCI, 20 AD
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46
13 YC, 9 HC, 7 MCI, 3 Possible AD, 14 AD
•
MCI: mild cognitive impairment, FTD: frontotemporal dementia
F-NAV4694
F-Florbetaben
F-Flutemetamol
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F-Florbetapir
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C-PiB
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Tracer
Figure 1. Overview of the level 2 centiloid calibration Summary
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The procedure to calibrate each tracer being identical, we will only describe it in general terms,
and will provide the final equations in the results section.
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For a given tracer, its PET images and associated PiB PET images were processed using the
standard SPM8 pipeline using the corresponding MR images. Neocortical SUVR for both the PiB
and tracer’s PET images were computed using the centiloid VOIs. Computing centiloid values for a
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new tracer using the standard SPM8 pipeline is a 2 steps process (Figure 1.a), where the tracer’s
transformed into centiloid using Eq 1.
i.
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SUVR (TracerSUVRStd*) is first transformed into PiB-calculated SUVR (PiB-calcSUVRStd*), before being
The first step is to compute the slope (TracermStd) and intercept (TracerbStd) of the tracer’s SUVR
against those of the corresponding PiB, so that
12'/32
~
12'/32
,
×
∗
+
12'/32
.
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∗
After checking that the R2 is above 0.7, TracerSUVRStd* is converted into PiB-calcSUVRStd* using the
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calculated slope and intercept, so that:
ii.
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/'0/
∗
=
12'/32
−
12'/32 ,
∗
12'/32
.
PiB-calc
SUVRStd* is then converted into centiloid using Eq 1.
Both equations are combined to directly transforms TracerSUVRStd* into centiloid.
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2.6. Level-2 Calibration of the 18F tracers using CapAIBL
In this section, we will run the level-2 calibration of each
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F tracer into centiloid using CapAIBL.
Similarly to the calibration using the standard method, the procedure to calibrate each tracer is
identical, and therefore we will only describe it in general terms, and provide the final equations in
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the results section.
For a given tracer, all the tracers’ PET images, as well as the associated PiB images were processed
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using CapAIBL. Neocortical SUVR for both the PiB and tracer’s images were computed. Computing
centiloid values for a new tracer using a non-standard method is a 2 steps process (Figure 1.b),
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where the tracer’s SUVR (TracerSUVRCapAIBL) is first transformed into PiB-calculated SUVR (PiBcalc
SUVRCapAIBL), before being transformed into centiloid using Eq 1. As specified in (7), the centiloid
computed using the standard method using PiB should always be used as a reference. Therefore,
the transform that converts (TracerSUVRCapAIBL) into PiB-calculated SUVR (PiB-calcSUVRCapAIBL), is
i.
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computed using the PiB SUVR from the standard method (PiBSUVRStd*) as the reference.
The first step is to compute the slope (TracermCapAIBL) and intercept (TracerbCapAIBL) of the tracer’s
'( ) * ~
12'/32
,
'( ) *
×
∗
+
12'/32
.
'( ) *
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12'/32
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SUVR against those of the corresponding PiB, so that:
After checking that the R2 is above 0.7, TracerSUVRCapAIBL is converted into PiB-CalcSUVRCapAIBL using
the calculated slope and intercept, so that:
/'0/
ii.
PiB-Calc
'( ) * = 12'/32
SUVR is converted into centiloid using Eq 1.
'( ) * −
12'/32 ,
12'/32
'( ) *
.
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Both equations are combined to directly transform TracerSUVRCapAIBL into centiloid.
2.7. Independent validation using images for the AIBL study
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In order to check the generalisability of the method outside the calibration dataset, the centiloid
values computed using both CapAIBL and the standard method should be compared in an
independent dataset. For each tracer, we used pairs of PET and MR images acquired as part of the
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Australian Imaging Biomarkers and Lifestyle (AIBL) study. The images were analysed using both
the standard pipeline and CapAIBL and the resulting SUVR were converted into centiloid using
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their respective equations. The centiloid values were then compared in terms of agreement, bias
and intra-class correlation (ICC). For the calculation of the ICC, we looked at the absolute
agreement, using a two-way mixed-effects model, with single rater.
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3. Results
3.1. Level-2 Calibration of CapAIBL using PiB
There was little bias between the PiB SUVR computed using CapAIBL and those computed using
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SPM, with the slope PiBmCapAIBL=0.991 and intercept PiBbCapAIBL=0.030. The R2 was 0.993, indicating
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good agreement between both methods. The combined equation that converts PiBSUVRCapAIBL into
CL was defined as:
'( ) *
= 100 ×
'( ) *
− 1.034
1.063
The mean centiloid for the YC using CapAIBL was -0.04, and 100.3 for the AD subjects. The
standard deviation in the YC was 4.40, compared to 4.34 when using the standard pipeline.
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A Scatter plot showing the centiloid values derived from CapAIBL against the ones derived from
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the standard pipeline is presented in Figure 2.
Figure 2. Scatter plot of PiB centiloid values from the calibration dataset computed using CapAIBL
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against the centiloid values computed using the standard method
3.2. Level-2 Calibration of CapAIBL using the 18F tracers
Conversion into PiB-equivalent SUVR
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The equations used to convert the SUVR from each tracers into PiB-CalcSUVR are presented in
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Table 2. The conversion equations computed using CapAIBL were similar to those produced
using the standard method. For both quantification methods, there was a good agreement
between the PiBSUVR and PiB-CalcSUVR, with a R2>0.89 for the standard method and R2>0.87 for
CapAIBL, well above the suggested threshold of 0.70. For both method, NAV had the
strongest agreement with PiB, followed by FLUTE, FBB and FBP.
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Table 2. Conversion equation for each 18F tracer SUVR into calculated-PiB SUVR using both SPM
SPM
/'0/
;
18
F-Florbetaben
0.991
− 0.389
0.610
0.953
− 0.220
F-Flutemetamol
0.768
;
18
F-Florbetapir
/'0/
ϯ
/'0/
@
@
− 0.046
0.980
1.008
;
'( ) *
− 0.382
0.948
0.655
;*>1?
0.962
'( ) *
− 0.240
0.960
0.827
;
0.893
'( ) *
− 0.532
0.870
0.534
'( ) * EP
'( ) * '( ) *
5ϯ
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*
− 0.518
0.517
'( ) *
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+ 0.073
1.090
;*>1?
18
/'0/
5∗
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F-NAV4694
CapAIBL
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7 8
18
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and CapAIBL
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Converting SUVR into centiloid
The equations used to convert the SUVR from each tracers into centiloid are presented in
Table Table 3. The scatter plots showing the corrected centiloid values from CapAIBL against
those from the standard method are presented in Figure 3. The agreement between the
centiloid computed using CapAIBL and those computed using the standard method was very
high (R2>0.97) for all tracers.
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Figure 3. Scatter plot of the corrected centiloid values computed for each 18F tracer using CapAIBL,
against the corresponding centiloid values computed for the same tracers using the standard
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method.
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Table 3. Equations to convert SUVR into centiloid.
CL Equation (SPM)
100 ×
7 8
18
F-NAV4694
100 ×
;
18
F-Florbetaben
100 ×
F-Flutemetamol
100 ×
− 1.033
1.170
100 ×
− 1.007
0.654
100 ×
F-Florbetapir
'( ) *
'( ) *
PiB
5ϯ
NA
0.993
− 1.068
0.990
0.988
1.082
;
'( ) *
0.702
;*>1?
− 1.046
'( ) *
100 ×
0.887
;
− 1.078
'( ) *
− 1.073
0.990
0.995
0.974
0.988
0.986
0.993
0.573
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100 ×
− 1.034
5∗
1.063
7 8
'( ) *
'( ) * TE
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@
− 1.042
0.554
100 ×
@ 12'/32
− 0.998
0.824
;
18
ϯ
100 ×
;*>1?
18
* 12'/32
− 1.014
1.073
Tracer
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C-PiB
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CL Equation (CapAIBL)
3.3. Validation on independent datasets from AIBL
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From the AIBL study, pairs of PET and MR images were selected for each tracer: 1000 pairs of
PiB/MR, 79 of 18F-NAV4694/MR, 119 of 18F-Florbetaben/MR, 446 of 18F-Flutemetamol/MR and 278 of
F-Florbetapir/MR. Relevant demographics for each tracer are presented in Supplemental Table 1.
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Scatter plot of the centiloid values computed using CapAIBL and the standard method are presented
in Figure 4. The centiloid values derived from PiB using CapAIBL were in good agreement with those
computed using the standard method (R2=0.990) and the slope of the regression shows a small
underestimation of less than 0.8%. With NAV, while the agreement was good (R2=0.986), the slope of
the regression showed an overestimation of 8.1%. With FBB, the agreement was also good
(R2=0.982), but the slope of the regression showed an underestimation of 11.4%. With FLUTE, the
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agreement was also good (R2=0.977), and the slope of the regression showed very little bias, with a
small overestimation of just 0.8%. With FBP, the agreement was a bit lower (R2=0.964), but the slope
of the regression showed very little bias, with an overestimation of just 1.3%. For all tracers, the
agreement defined by ICC was excellent (ICC>0.98). While the centiloid scale is not designed for
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image visualisation, all the images can now be presented using the same colour scale. Example
images for each tracer from the AIBL study are presented in Supplemental Figure 1 for centiloid
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values of 0, 25, 50, 75 and 100.
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Figure 4. Scatter plot of the corrected centiloid values computed on the AIBL dataset for each
18
F
tracer using CapAIBL, against the corresponding centiloid values computed for the same tracers using
the standard method.
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4. Discussion
This study showed that the PET-only quantification method, CapAIBL, can be used to perform
centiloid quantification using the most commonly used Aβ PET tracers. CapAIBL generated SUVR
values that were in very good agreement with those generated using the standard pipeline for all
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tracers on the calibration data. We have also illustrated how a new method can be calibrated to
generate centiloid values.
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On an independent dataset from the AIBL study, the centiloid values computed for PiB, FLUTE and
FBP using the standard pipeline and CapAIBL were in very good agreement, with very little bias,
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which would indicate that the method generalises well and can be used in other centres. With NAV
and FBB however, while the R2 was high, there was significant bias between the methods. There are
several possible causes for this.
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Size of dataset. We had access to much a smaller dataset for NAV and FBB compared to PiB, FLUTE
and FBP which could make the estimate of the regression parameters less accurate. Moreover, for
these 2 tracers, there was a greater proportion of subjects with very low CL value (54% of subjects
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imaged with FBB and 57% of subjects imaged with NAV have CL<10, compared to 43% and 49% for
FLUTE and FBP). Therefore, a few positive outliers could influence the estimation of the regression
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parameters. While only a limited dataset was available at the time of this study, we will be able to
further refine the estimate of the regression parameters as more images become available.
Scanner effect. The use of different scanners could bias the SUVR estimates with CapAIBL, as
different point spread functions, reconstruction algorithms, and acquisition parameters can alter the
appearance of the PET images (15,16). This could in turn introduce a bias during the estimation of the
adaptive atlas and the spatial normalisation to that atlas. To further examine this, we compared the
scanners used for the acquisition of the calibration scans and the AIBL scans for NAV and FBB. All the
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calibration scans for NAV were acquired on a Philips Allegro, and in AIBL, 55 of the scans were
acquired on the same scanner, while 24 were acquired on a Philips Gemini. In addition to different
scanners being used, different reconstruction algorithm were employed, with a 3D row action
maximum likelihood algorithm (3D RAMLA) used on the Allegro, and line of response RAMLA (LOR
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RAMLA) on the Gemini. Figure 5 presents the scatter plot of the Centiloid values computed using
CapAIBL against the ones computed using the standard method segregated by scanner model. By
restricting the AIBL dataset to the scans acquired on the same scanner as the calibration dataset
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(Allegro scanner), the error is reduced to a slight overestimation of 2.6%. With the scans acquired on
the Gemini scanner, the error is now much larger, with an overestimation of 14.5%. This would
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indicate that the SUVR computed using CapAIBL are scanner dependant, and that by using the same
scanner on both the calibration dataset and the independent dataset, very accurate quantification
can be obtained. We could not replicate a similar plot with FBB, as most of the calibration scans
(29/35) were acquired on the Gemini scanner, while most of the AIBL scans (112 /119) were acquired
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on the Allegro scanner. However, the differences observed in NAV, where the CL computed using
CapAIBL was about 12% higher in scans acquired on the Gemini scanner compare to those acquired
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on the Allegro scanner, is comparable to the differences seen in FBB, where the AIBL scans acquired
using the Allegro had CL 11.4% lower than what they should be given the calibration data that were
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acquired on the Gemini scanner. From the available data, we can estimate that using CapAIBL, there
is a 12% difference in CL measures between the Allegro and Gemini scanners in both NAV and FBB,
with CL computed on Gemini scans being higher than the ones from Allegro, and that the difference
in scanner can explain most of the differences observed between the centiloid values computed
using CapAIBL and the standard method in the AIBL dataset. While here, we assumed that the
standard method provided the ground truth, and only focused on the differences between CapAIBL
and the standard method given different scanners, it is worth noting that some of these scanner
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specific differences could also introduce biases when using the standard pipeline, as previous studies
have shown inter-scanner image variability in multi-centre studies, requiring scanner specific
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calibrations (15,16).
Figure 5. Scatter plot of the corrected centiloid values computed on the AIBL dataset for NAV for each
scanner using CapAIBL, against those computed using the standard method.
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Tracer specific differences. Lastly, different tracers might be more susceptible to exhibit large
differences when different scanner/reconstruction method are used, which would in turn produce
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differences in the CL values computed using CapAIBL.
With the current data at hand, it is hard to pin point to the exact origin of some of the differences
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observed between the CL values computed using CapAIBL and the standard method. There is
however a very strong indication that the use of different scanners and reconstruction algorithm
could be responsible for some of these differences. The exact source of these differences, and how
much of an effect they have on centiloid values computed using different methods and different
tracers needs to be further examined. In the mean-time, since the bias appears to be a linear
transform, this could be accounted for using a scanner specific calibration transform. While this
cannot account for potential differences in the CL values computed using the standard method with
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different scanner, it can ensure that CL values computed using CapAIBL are consistent with the ones
computed using the standard method.
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5. Conclusions
This is the first study showing the calibration of SUVR values produced using a non-standard PET
quantification method to the centiloid scale. Using CapAIBL, we showed that reliable centiloid
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estimates could be obtained using a PET-only quantification method, which could facilitate its
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adoption in the clinic, as the centiloid values could be produced directly on the PET scanner. The bias
observed in NAV and Florbetaben images from the AIBL study indicates that a scanner specific linear
correction might be required. Further work is required to better understand and characterise the
variability induced by the use of different scanners and reconstruction algorithms in both CapAIBL
Disclosures
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and the standard quantification pipeline.
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Vincent Doré, Olivier Salvado, Jurgen Fripp, Chris Rowe, Victor Villemagne are inventors on patent
US9361686B2 that describes some aspects of the software CapAIBL. Chris Rowe has received
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research grants from Piramal Imaging, GE Healthcare, Cerveau, Astra Zeneca, Biogen. Victor
Villemagne is and has been a consultant or paid speaker at sponsored conference sessions for Eli
Lilly, Piramal Imaging, GE Healthcare, Abbvie, Lundbeck, Shanghai Green Valley Pharmaceutical Co
Ltd, and Hoffmann La Roche.
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