Maximum Likelihood Processing of Pileup in Scintillation Cameras

1. Maximum Likelihood Processing of Pileup in Scintillation Cameras Neal ClinthorneDivision of Nuclear MedicineSam HuhDepartment of Biomedical Engineering University of MichiganAnn Arbor, MI USA

2. Compton 2nd Detector Countrate

3. 2nd Detector for Compton • Pixellated LSO panel detector • Good countrate capability • Good spatial resolution • Good timing performance • Expensive! • Pixellated LaBr3? • Same benefits • Same problems + extremely hygroscopic • Pixellated NaI(Tl)? • Expensive • Slow decay (240 ns) • Continuous NaI(Tl) (Anger camera) • Cheap • Even more problems with pileup due to spreading of light Can continuous NaI(Tl) work?

4. Anger Camera Review • Gamma-ray interacts in scintillator generating light • Light spreads out among PMTs • Position determined by relative outputs of PMTs • Energy determined by summed outputs of PMTs

5. Temporal Pileup Due to Slow Decay

6. Temporal Pileup Due to Slow Decay

7. Shorter Integration Times? • Shorter integration times reduce pileup • They also decrease resolution

8. State of the Art Correction Wong, et al., IEEE Trans. Nucl. Sci. 45(3):1122–1127, 1998 But what about spatial correlations due to spreading of scintillation light?

9. PMT Outputs at 100K CPS Simulation Model of Anger Camera with 9 x 75mm dia PMTs Pulse Height Time (seconds)

10. PMT Outputs at 1M CPS Pulse Height Time (seconds)

11. PMT Outputs at 4M CPS Pulse Height Time (seconds)

12. Modified Scintillation Camera Scintillation Camera 100 MHz ADCs PMT PMT Processing Computer Scattering Detector PMT PMT Timing Signals Event Trigger –> Xfers ADC data Timing Signals Coincidence Unit Output from one PMT Countrate ~1Mcps

13. Single Pulse Likelihood

14. Multiple Pulse Likelihood Estimate energy & position for each of the k pulses

15. Computationally difficult Maximization? “Complete data” is tkmn – with this choice, maximization is trivialDon’t know tkmn but that’s what the Expectation-Maximization (EM) algorithm is for.

16. EM Example

17. EM Example EM algorithm allows photoelectrons to be associated with particular event Of course, it may select wrong but in the end it maximizes the likelihood

18. Performance 100Kcps Y Position Error (mm) X Position Error (mm)

19. Performance 1Mcps Y Position Error (mm) X Position Error (mm)

20. Performance 2Mcps Y Position Error (mm) X Position Error (mm)

21. Performance 2Mcps

22. Performance 4Mcps

23. Performance 6Mcps

24. 1 of 8 channels / board -G -3 1 PMT in Channel out Variable gain ∑ From other7 channels “Energy” out CFTD Variable delay Electronics Coincidence trigger

25. High Noise Varying Baseline Typical PMT Signals

26. Separable due to different positions Two point sources 38 49 54 50 55 53 40 51 52 PMT Array Waveform “snapshot” Pileups

27. Initial Grid Image from 2nd Detector 140 keV – countrate performance yet untested

28. Summary • Pileup is an important issue in practical Compton camera 2nd detector in medical applications • Present pileup rejection methods work fairly well up to moderate countrates but ignore important information in scintillation pulse • New ML method performs better at high countrates than existing methods but computationally expensive • May be possible to apply “band-aid” to Li-Wong method • Next step will apply methods to recently completed camera