Presentation on theme: "Variability of PET-PIB retention measurements due to different scanner performance in multi-site trials Jean-Claude Rwigema Chet Mathis Charles Laymon."— Presentation 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 18 F 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 (DV in a receptor region) (DV in a non-receptor containing region)
FRC (Frontal Cortex) ACG (Anterior Cingulate) CER (Cerebellum) Time Activity Curve from PET
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 One example from mci004 ACG ROI
Outcome Measure (DVR) P-value: MCI001MCI002MCI E E-17 ROI DVR
Control MCI (PIB+) Parametric images of Logan DVR Logan DVR
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 Pitt Mich R value
Reconstruction in U of Pitt Parametric images of Logan DVR Reconstruction in U of Mich Logan DVR
ln Recon. | UPitt – UMich | | test – 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