1. 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

2. 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.

3. 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

4. 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

5. 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

6. 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

7. 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)

8. ROIs from MR Image FRC (Frontal Cortex) ACG (Anterior Cingulate) CER (Cerebellum)

9. FRC (Frontal Cortex) ACG (Anterior Cingulate) CER (Cerebellum) Time Activity Curve from PET

10. 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

11. Outcome Measure (DVR) P-value: MCI001MCI002MCI004 5.18E-15 0.511 4.23E-17 ROI DVR

12. Control MCI (PIB+) Parametric images of Logan DVR Logan DVR 0.5 2.5

13. 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 0.96 0.98 0.93 0.96 Pitt Mich R value 0.96 0.98 0.97 0.96

14. Reconstruction in U of Pitt Parametric images of Logan DVR Reconstruction in U of Mich Logan DVR 0.5 2.5

15. 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

16. 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.

17. Conclusion Recon/recon variability depends on the degree of regional PIB retention with high levels of uptake showing greater recon/recon variability.

18. 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

19. 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

20. 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 