李志傑 Chih-Jie Lee Center for Gamma-Ray Imaging, College of Optical Sciences,

1. Department of Computer Sciences and Information Engineering,National Cheng Kung UniversityDesign and Assessment of Cardiac SPECT Systems 李志傑 Chih-Jie Lee Center for Gamma-Ray Imaging, College of Optical Sciences, University of Arizona 03/15/2013

2. Why Cardiac Imaging ?

3. Heart Disease Facts from CDC Heart disease is the leading cause of death for both men and women. In 2006, a total of 631,636 people in the United States died of heart disease. (26%) In the United States, every 34 seconds, someone has a heart attack. Each minute, someone dies from a heart disease-related event. In 2010, heart disease will cost the United States $316.4 billion. The most common type is coronary artery disease Half of the men who die suddenly of coronary heart disease have no previous symptoms. Cardiac imaging allows specialists to detect heart disease in its earliest and most treatable stages. 3

4. Outline • Research Background • Observer study • System simulation and optimization

5. Background: SPECT Imaging & Imaging Equation • Single Photon Emission Computed Tomography, 99mTc, 140 keVγ-ray • Imaging Equation: Object (3D) g=Hf + n noise Image recorded (2D) Transformation matrix

6. System prototype:GE Discovery NM 530c Commercialized in August, 2009 • 9 detectors/row, 3 rows Imaging time: 3 mins Commercial Product : Only 19detectors

7. Assessment Implementation: • Object f: • Clinical studies are expensive and time-spending • 44,000 images tested in this study • System: • US $ 50K per CZT detectors • Cost more than US $ 1 million • Do the assessment BEFORE manufacturing • Everything is by simulation • Verified with one clinical data set from GE

8. NCAT (simulated object f) • ’The NURBS-based cardiac torso (NCAT) software package uses non-uniform rational B123 spline (NURBS) surfaces to realistically model human anatomy and physiology for medical 124 research. NCAT also includes a realistic model of the cardiac motion based on tagged MRI data of a normal patient.’ (P. Seagars, PhD dissertation)

9. Bull Eye Coordination • Defect R=2,3,…,8 mm • 3 defect locations • LAD: Left Anterior Descending artery • LCX: Left Circumflex artery • RCA: Right Coronary Artery

10. Cardiac Defects (All) 3D object from NCAT NCAT (object f) LCX Exampled image (projection g) RCA LAD

11. Imaging Equation • Cardiac SPECT: 4-D case (time-dependent) • Electrocardiography: ECG • Usually 8 or 16 gated • Trade-off between: • # of gated images • imaging time • f1(θ) …. f8(θ) are assumed time-independent • g1(θ) …. g8(θ) for f1(θ) …. f8(θ) ECG plot (One heart beat) 1 2 3 4 5 6 7 8 Volt Time

12. Reconstruction Algorithm: MLEM(Maximum Likelihood Expectation Maximization) Raw Projection data (clinically measured) Reconstructed 3-D images 27 cams, 32 x 32 pixels 81 x 81 x 81 voxels MLEM We do NOT need reconstructions if we use scanning linear observer

13. Generate simulated projections

14. Research Background • H: Hypothesis • H0: signal-absent • H1: signal-present • D: Decision • D0: Decided signal-absent • D1: Decided signal-present • TP : H1 and D1 • TPF, FPF will be applied in ROC curves

15. Research Background • Receiver Operating Characteristic (ROC) curve

16. Research Background • LROC : Localization ROC curve • Definition of true positives • Presence • Location of signals • EROC : Estimation ROC curve • Definition of true positives • Presence • Parameters of signals: • Locations • Sizes • Etc …

17. Research Background • EROC curves • Figure of merit • AUEC: Area under EROC curves • σAUEC: *One shot error (different from error bars) • TPF value at 5% FPF *From One shot LROC : Kupinski et al. http://osx-server.optics.arizona.edu/~kupinski/Matthew_Kupinski/LROC_Software.html

18. Definition of True Positives • Definition of “LR1” • SLO needs to predict the class of correct location and the error or radius estimation is smaller than 1 mm • Defect @ LAD 5mm: “LR1” SLO: LAD 4,5,6 mm O; LAD 3mmX

19. Outline • Research Background • Observer study (for ANY imaging systems) • Human observer : doctors • 3D reconstructions required • Model observer • 2D or 3D as designated • System simulation and optimization

20. Scanning Linear Observer Signal with some specific parameter Mean background constant Covariance matrix Tested image Output Whitaker, M. K., E. Clarkson, and H. H. Barrett ”Estimating random signal parameters from noisy images with nuisance parameters: linear and scanning-linear methods,” Optics Express, Vol. 16, No. 11, 26 May 2008 C.-J. Lee, M. A. Kupinski, L. Volokh, ”Assessment of Cardiac Single-photon Emission Tomography Performance Using a Scanning Linear Observer,” Medical Physics, Vol. 40, No. 1, January 2013

21. Scanning Linear Observer:The method to apply the SLO • 21 Kg’s : • KLAD2…. KLAD8 : Defect at LAD / radius = 2~8 mm • KLCX2…. KLCX8 : Defect at LCX / radius = 2~8 mm • KRCA2…. KRCA8 : Defect at RCA / radius = 2~8 mm

22. Outline • Research Background • Observer study • System simulation and optimization

23. Test Effects: # of Detectors Per GE’s requests

24. Pinhole configurations • Three configurations tested: F, S, V • F: Fixed • all 4.5 mm diameter • S: Sensitivity

25. Pinhole configurations • V: ‘v’aried Rg: planar spatial resolution (from pinhole blurring) Ri: detector intrinsic resolution Rentmeester, M. C. M., F. van der Have, and F. J. Beekman, ``Continuous Model of Multi-Pinhole SPECT Devices'', 2005 IEEE Nuclear Science Symposium Conference Record}, 2005, pp. 1777-1781.

26. Radii of Pinholes:

27. Test Effects: Distributions of 19 Detectors 1: closest to object 2: No side-row central cams 3: Half 1 & 2 4: Fish-bone 5/6: No central cams 7/8: 2 row / central 5 cams 9: Least overlap liver & heart 10: ~ 2 rows Stepwise: best/worst 11/14: for ‘F’ 12/15: for ‘V’ 13/16: for ‘S’

28. Stepwise Procedure:

29. : More important : Less important

30. Relative Sensitivities: GE19F Sensitivity: 0.003683 = 0.368 %

31. Results • EROC curves • Number of detectors + Pinhole configurations • Distribution of detectors + Pinhole configurations

32. Results • EROC curves • Number of detectors + Pinhole configurations • Distribution of detectors + Pinhole configurations

33. Test Effects: # of Detectors

34. EROC / AUEC : LR1 GE27 F/S/V AUEC : LR1 One shot error * *From One shot LROC : Kupinski et al. http://osx-server.optics.arizona.edu/~kupinski/Matthew_Kupinski/LROC_Software.html

35. EROC / AUEC : LR1 GE19 F/S/V AUEC : LR1 One shot error * *From One shot LROC : Kupinski et al. http://osx-server.optics.arizona.edu/~kupinski/Matthew_Kupinski/LROC_Software.html

36. EROC / AUEC : LR1 GE13 F/S/V AUEC : LR1 One shot error * *From One shot LROC : Kupinski et al. http://osx-server.optics.arizona.edu/~kupinski/Matthew_Kupinski/LROC_Software.html

37. EROC / AUEC : LR1GE27 / GE19 / GE13 F AUEC : LR1 One shot error * *From One shot LROC : Kupinski et al. http://osx-server.optics.arizona.edu/~kupinski/Matthew_Kupinski/LROC_Software.html

38. EROC / AUEC : LR1 AUEC : LR1 One shot error * *From One shot LROC : Kupinski et al. http://osx-server.optics.arizona.edu/~kupinski/Matthew_Kupinski/LROC_Software.html

39. LROC TPF @5% FPF: LR1 Same as previous EROC but magnified(FPF: 0 ~ 0.1) TPF @ FPF = 5% • TPF = 80% : σall=0.742% • TPF = 75% : σall=0.751% • TPF = 40% : σall=0.767%

40. Results • EROC curves • Number of detectors + Pinhole configurations • Distribution of detectors + Pinhole configurations

41. Test Effects: Distributions of 19 Detectors ‘F’ :Best :Worst

42. 4 systems w/ ‘F’ pinhole : Stepwise predicted designs AUEC TPF @ 5% FPF 75.73% 76.46% 0.8542 0.8517 72.45% 72.14% 0.8149 0.813 • σ 11F : 0.0027 • σ 03F : 0.0027 • σ 14F : 0.0029 • σ 07F : 0.0029 • TPF = 80% : σall=0.742% • TPF = 75% : σall=0.751% • TPF = 40% : σall=0.767%

43. Test Effects: Distributions of 19 Detectors ‘V’ :Best :Worst

44. 4 systems w/ ‘V’ pinhole : Stepwise predicted designs AUEC TPF @ 5% FPF 75.06% 75.04% 0.8596 0.8592 69.82% 0.7981 68.94% 0.7931 7V (3rd worst) : 0.8005 7V (2nd worst):69.48% • σ 08V : 0.0023 • σ 12V : 0.0027 • σ 10V : 0.0029 • σ 15V : 0.0030 • TPF = 80% : σall=0.742% • TPF = 75% : σall=0.751% • TPF = 40% : σall=0.767%

45. Test Effects: Distributions of 19 Detectors ‘S’ :Best :Worst

46. 4 systems w/ ‘S’ pinhole : Stepwise predicted designs AUEC TPF @ 5% FPF 71.86% 0.8257 70.76% 0.8202 0.7945 67.91% 67.01% 0.7806 • σ 13S : 0.0029 • σ 12S : 0.0030 • σ 05S : 0.0031 • σ 16S : 0.0032 • TPF = 80% : σall=0.742% • TPF = 75% : σall=0.751% • TPF = 40% : σall=0.767%

47. Conclusions • SLO + EROC • Able to assess (any) imaging systems in 2D • No reconstructing process needed • More # of samples, more time saved. • ~60% time saved in our case (44,000 images) compared with reconstructing to 2mm/6mm multi-grid voxels • Quick Summary of Tested Effects: • # detectors ~ Pinhole > distribution • Pinhole configuration should be chosen carefully • ‘Distribution’ can figure out which detectors are important

48. Conclusions: Tested Effects details • # of detectors: • The more, the better • But more expensive, $50k / detector crystal • The results show: • GE27  GE19 : the results are close, save $400k • GE27  GE13 : relatively large gap • GE decided that “19” is the final # of detectors • Distribution of detectors • Minor effects, but still can affect the performance • Stepwise procedure is a good direction

49. Conclusions • Pinhole configurations • ‘F’ : seems to be best if consider TPF @ FPF=5% • ‘S’ : try to trade sensitivity with resolution, but the performances are not good • ‘V’ : largest sensitivity, TPFs @ 5% FPF are lower than ‘F’

50. Q&A Thank you ! ?