variability of pet-pib retention measurements due to different scanner performance in multi-site...
TRANSCRIPT
Variability of PET-PIB retention measurements due to different
scanner performance in multi-site trials
Jean-Claude Rwigema
Chet Mathis
Charles Laymon
Jonathan P.J. Carney
Tae Kim
University of Pittsburgh, Radiology
University of Pittsburgh, Medical School
Faculty
PIB (Pittsburgh Compound B)
• Amyloid- (A) plaque deposition is a pathological hallmark of
Alzheimer’s disease (AD)
• Pittsburgh compound-B (PIB) is a radiotracer used in positron
emission tomography (PET) that binds to amyloid plaques and is
a valuable tool in the development and evaluation of anti-
amyloid therapeutics.
Introduction
• Drug development requires large numbers of research subjects with
the concurrent need for large multi-site trials.
• AD longitudinal studies may be of long duration
• Different sites may run different software versions
• Software may be upgraded during a longitudinal study
Attenuation image U Mich ReconstructionU Pitt Reconstruction
Bone-like
Water Air
Emission image
Water with 18F
• Phantom data show that image reconstructions by U of Pitt and by U of Mich have differences. Differences are mainly attributable to differences in scatter correction implementation.
Reconstructions of Phantom Data acquired at the University of Michigan
• We investigate the variability in PET-based
measures of PIB retention due to site-to-site
differences in comparison to the variability
between individual test and retests in the same
scanner.
Aim
• Data was acquired at U Mich
• Each subject was scanned once, and rescanned for comparison
• Data was reconstructed at UPitt and UMich
• Each site operates the same model PET scanner (Siemens
HR+), but different versions of processing software (different
scatter corrections)
• Four subjects were evaluated (one control and three mild
cognitive impairment (MCI) subjects)
Methods
Structural Magnetic resonance (MR) Imaging
-1.5 T GE Signa using SPGR
- Skull-cropped images reoriented along AC-PC line
- Coregister MRI and PET
Positron Emission Tomography (PET) Imaging
- Dynamic [11C]PIB study (15 mCi, 90 min, 34 frames)
- MR-guided region definition (ROI)
- PIB retention was assessed using the PIB distribution volume ratio
(DVR) value determined via the Logan graphical analysis, using
cerebellum data as input
Methods
)(
)(
p
t
CPlasmainTracerofionConcentrat
CTissueinTracerofionConcentratDV
refDV
DVDVR
(DV in a receptor region)
(DV in a non-receptor containing region)
ROIs from MR Image
FRC (Frontal Cortex)
ACG (Anterior Cingulate)
CER (Cerebellum)
FRC (Frontal Cortex)
ACG (Anterior Cingulate)
CER (Cerebellum)
FRC
-0.2
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40 50 60 70 80 90
min
uCi/m
l
Control
MCI (AD)
MCI (Control)
ACG
-0.2
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40 50 60 70 80 90
min
uCi/m
l control
MCI (AD)
MCI (control)
Time Activity Curve from PETCER
-0.2
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40 50 60 70 80 90
min
uCi/m
l Control
MCI (AD)
MCI (Control)
DVR value obtained by Logan analysis
In steady-state, with graphical analysis
Where C(t) is the radioactivity measured by PET at time t in a specified ROI,CB is radiotracer concentration in the non-receptor region
int')()(
)(00
TC
CBdtDVR
TC
dttCTT
y = 2.6887x - 31.091
R2 = 0.9998
20
40
60
80
100
120
20 30 40 50 60
)(
)(0
TC
dttCT
)(/0
TCCBdtT
One example from mci004 ACG ROI
Outcome Measure (DVR)
P-value: MCI001 MCI002 MCI004
5.18E-15 0.511 4.23E-17
0.0
0.5
1.0
1.5
2.0
2.5
3.0
FRC ACG PRC PAR LTC OCC SMC MTC SWM PON
Control
MCI_002
MCI_001
MCI_004
ROI
DVR
Control
MCI (PIB+)
Parametric images of Logan DVR
Logan DVR
0.5
2.5
0.5
1
1.5
2
2.5
3
0.5 1 1.5 2 2.5 3
test
rete
st
con001
mci001
mci002
mci004
Comparison of DVR values for test vs. retest
The variability of test-rest (= test – retest / test) was 5.4 ± 2.7 % (Pitt) 5.4 ± 2.2 % (Mich)
R value
0.960.980.930.96
0.5
1
1.5
2
2.5
3
0.5 1 1.5 2 2.5 3
test
rete
st
con001
mci001
mci002
mci004
Pitt Mich
R value
0.960.980.970.96
0
0.5
1
1.5
2
2.5
3
0 0.5 1 1.5 2 2.5 3
Mich
Pit
t con1 (test)
con1 (retest)
mci1 (test)
mci1 (retest)
mci2 (test)
mci2 (retest)
mci4 (test)
mci4 (retest)
Reconstruction in
U of Pitt
Parametric images of Logan DVR
Reconstruction in
U of Mich
Logan DVR
0.5
2.5
R2 = 0.1579
R2 = 0.2598
R2 = 0.662
R2 = 0.6686
-4
-3
-2
-1
0
1
2
3
4
0.5 1 1.5 2 2.5 3
DVR
mci001 (test)
mci001 (retest)
mci004 (test)
mci004 (retest)
lnR
econ
. | U
Pit
t – U
Mic
h |
| tes
t – r
etes
t |
R2 = 3E-05 R2 = 0.0013
R2 = 0.007
R2 = 0.0233
-5
-4
-3
-2
-1
0
1
2
3
0.5 1 1.5 2 2.5 3
DVR
con001 (test)
con001 (retest)
mci002 (test)
mci002 (retest)
MCI (PIB+) Control and MCI (PIB-)
Recon/recon DVR variance was significantly higher than test/retest variance
in high PIB uptake areas (high DVR)
Variability vs. DVR
Summary
• PIB retention from two of MCI subjects showed PIB+ results, with
significant uptake distributed similarly to that found in subjects with
AD.
• One MCI subject showed PIB- behavior with relatively little PIB
uptake.
• The variability of test-retest was small.
• Recon/Recon DVR variance was significantly higher than test/retest
variance in high PIB uptake areas (high DVR) in PIB+ MCI, while
such variances were comparable in lower uptake areas in control
and PIB- MCI where PIB uptake was uniformly low.
Conclusion
Recon/recon variability depends on the degree of regional PIB retention with high levels of uptake showing greater recon/recon variability.
Acknowledgments
PET center
Chet Mathis, Ph.D.
Jonathan P.J. Carney, Ph.D.
Charles Laymon, Ph.D
Michele Bechtold
MNTP program
Seong-Gi Kim, Ph.D.
William Eddy, Ph.D.
Tomika Cohen
Rebecca Clark
Scatter Correction
• Simulation-based scatter correction:
- Analytical simulation: single-scatter
simulation: use transmission/emission for
calculating single coincidence rate
- Monte Carlo simulation: compute scatter
estimation from the fundamental physics of the
Compton scattering process
• Energy window approach: photons at energy
below sudden threshold must be scattered photons
• Energy window approach: photons at energy
below 511 keV must be scattered photons
• Convolution and deconvolution approach: the use of a scattering
“kernel” function to correct the sinogram via convolution-subtraction
or deconvolution
• Simulation-based scatter correction:
- Analytical simulation: single-scatter simulation
- Monte Carlo simulation: compute scatter estimation from the
fundamental physics of the Compton scattering process
Scatter Correction