The Forefront in Image Processing: PET/Molecular Approaches

1. The Forefront in Image Processing: PET/Molecular Approaches Joel Karp University of Pennsylvania Sixth Annual NCI-Industry Forum Quantitative Oncologic Imaging April 7-8, 2005

2. Issues of Performance, Image Processing, Quantification • Performance of current-generation PET scanners Global effects - data correction Local effects - image reconstruction Statistical and count-rate effects • Self-consistency: instrument performs same day-to-day • Cross-consistency: all instruments produce same result • Comparing images (PET and CT) from different patients, different instruments, and different institutes

3. What is Measured with PET b Random coincidence (~2t. singles2) True coincidence a Scattered coincidence Coincidence? • Yab= Nab(AabTab+Sab+Rab) Record event Trues What is measured Normalization Attenuation Scatter Randoms

4. Reconstruct image from line-of-response (LOR) projection data If there are N counts in the image, SNR ≠ N / (N)1/2 Signals from Different Voxels are Coupled  Statistical Noise Does Not Obey Counting Statistics

5. x(0) Iterative reconstruction Data A x = y y Back-projection c(k) Differ ence d(k) Correction for Attenuation, Scatter, Randoms Start here Update Forward projection y(k) x(k) ^ Image

6. Data Flow DETECTOR Philips Allegro: 616 x 29 crystals RAWVIEW (52 bytes/event) For A,B side of event 26 PMT energies/zone (26 bytes) 100M events = 5200 Mbytes DIGITIZER POSITION CALCULATOR LISTVIEW (8 bytes/event) For A,B side of event 2D position (3 bytes) timestamp Energy (1 byte) TOF (1 byte) 100M events = 800 Mbytes BINNER SINOGRAM (80 Mbytes/frame) R, Phi (295x161x2 = 95 Kbytes) Slice (29^2 = 841) 100M events = 560 Mbytes (7 frames) COMPUTER IMAGE X,Y (128x128 = 16 Kbytes) 100M events = 4 Mbytes (250 slices) reconstruction PACS archive

7. True Scatter Low Scatter and Randoms High Scatter and Randoms 2D (septa) vs. 3D (no septa) 2D Imaging 3D Imaging S T R Low geometric sensitivity High geometric sensitivity Scatter decreases with high energy threshold - depends on energy resolution

8. Singles FOV 3D mode Out-of-field activity increases randoms in 3D Problem increases as bore size increases -> less shielding • Randoms ~ 2t. Singles2 • decreases with narrow timing window (2t) • decreases with high energy threshold • estimated (and subtracted) with 2nd (delayed) timing window

9. Count-rate Performance 10 mCi dose 70-cm long x 20-cm diameter NEMA 2001 (body) Noise Equivalent Count-rate NEC = T/(1+S/T+R/T) NEC ~ SNR2 Philips Allegro

10. PET Imaging Performance • Spatial resolution -> partial volume effect intrinsic: 4-6 mm reconstructed: >10 mm • Scatter fraction -> noise and bias (after correction) 2D: 10-20% SF 3D: 30-60% SF • Sensitivity and count-rate capability -> statistical quality 25 - 100 kcps or 5 M - 20 Mevents per 3 min frame

11. A B Scatter Correction Before Scatter correction After Scatter correction Single Scatter - Model based correction Calculate the contribution for an arbitrary scatter point using the Klein-Nishina equation

12. Attenuation correction with radioisotope transmission scan 20 mCi 137Cs source - 662 keV A = 1 / e -md d = length of chord through tissue m = attenuation coefficient

13. Attenuation correction for PET Types of transmission images Single photon Cs-137 (662 keV) lower noise 5-10 min scan time some bias lower contrast X-ray (~30-140kVp) no noise 1 min scan time potential for bias high contrast Coincident photon Ge-68/Ga-68 (511 keV) high noise 15-30 min scan time low bias low contrast

14. Attenuation/Scatter correction No AC or Scatter Corr AC and Scatter Corr University of Pennsylvania PET Center Philips Allegro

15. Fore-FBP 3D Ramla Fully 3D Iterative Reconstruction improves image quality How about quantification?

16. 10 mm 37 mm 13 mm 28 mm 22 mm 17 mm NEMA NU2-2001 Image Quality Phantom foam Out-of-field Activity

17. Partial Volume Effect

18. NEMA IEC Phantom LOR RAMLA reconstruction Vary relaxation parameter l from 0.00025 (top left) to 0.075 (bottom right)

19. Contrast vs. Noise Iterative - Ramla Filtered Backprojection (FBP) 1.7 cm hot sphere 2.8 cm cold sphere

20. Image processingFilters for restoring the spatial frequency components Low (left) - Maximum gain = 2.5 Medium(middle)- Maximum gain = 3.5 High gain (right) - Maximum gain = 4.5 k - parameter describing the Gaussian roll-off fcut - cutoff frequency K, fcut -were bracketed from an analysis of phantom data WF(f) = 1/MTF(f) for f<fcut WF(f) = 1/MTF(fcut) exp-kf 2 for f>fcut

21. Lesion contrast improves with filtering Profile through the lesion no low med high

22. 5Mcts 5Mcts TOF 1Mcts TOF 1Mcts no TOF 300 ps TOF Time-of-Flight : list-mode iterative reconstruction 1 Mcts 5 Mcts 10 Mcts

23. Challenges in comparing images • Spatial resolution differences partial volume - simple (approximate) correction spatial recovery in reconstruction model adds noise • Reconstruction algorithm local convergence depends on algorithm and activity • Accuracy of corrections - randoms, scatter, attenuation depends on patient size and activity distribution • Imaging protocol scan acquisition time and delay post-injection • Quantification - typically based on simple cylinder QC - monitor and correct daily drifts Activity calibration - counts/voxel/min -> nCi/ml Count-rate corrections - dead-time

24. Challenges in comparing images • Instrumentation in PET is constantly evolving performance of new scanner >> older scanner • Image data size is large - data transfer and archiving PET: 4 Mbyte (with 4 mm3 voxels) CT: 64 Mbyte (with ~1 mm3 voxels) • DICOM quantification (SUV) requires PT format (not NM) manufacturers workstations still most practical • Data analysis tools must be standardized and validated region-of-interest • Image processing behavior must be understood - difficult to standardize