1 / 24

Fisher information-based evaluation of image quality for time-of-flight PET

Fisher information-based evaluation of image quality for time-of-flight PET. Kathleen Vunckx 1 , Lin Zhou 1 , Samuel Matej 2 , Michel Defrise 3 , Johan Nuyts 1.

isra
Download Presentation

Fisher information-based evaluation of image quality for time-of-flight PET

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Fisher information-based evaluation of image quality for time-of-flight PET Kathleen Vunckx1, Lin Zhou1, Samuel Matej2, Michel Defrise3, Johan Nuyts1 1 Dept. of Nuclear Medicine, Katholieke Universiteit Leuven, Leuven, Belgium.2 Dept. of Radiology, University of Pennsylvania, Philadelphia, USA. 3 Dept. of Nuclear Medicine, Vrije Universiteit Brussel, Brussels, Belgium.

  2. Outline • Introduction: TOF PET • Aim & motivation • Image quality evaluation methods • Simulations & results • Conclusions & future work

  3. Conventional PET Time-of-flight PET Introduction: TOF PET Time annihilation  detection: Annihilation location: Localization uncertainty: Time resolution

  4. Slice A Slice B CT CT non-TOF non-TOF TOF TOF Introduction: TOF PET Philips Gemini TF PET/CT Courtesy of S. Matej et al.

  5. Outline • Introduction: TOF PET • Aim & motivation • Image quality evaluation methods • Simulations & results • Conclusions & future work

  6. Aim & motivation • Effect TOF information on image quality? • Influence TOF kernel accuracy on image quality? Time difference=Extra information Conventional PET Time-of-flight PET

  7. Outline • Introduction: TOF PET • Aim & motivation • Image quality evaluation methods • Tomitani’s analytical TOF gain calculation • Fisher information-based evaluation method for TOF PET • Simulations & results • Conclusions & future work

  8. Dx D Tomitani’sanalytical TOF gain calculation non-TOF PET** TOF PET* Restrictions: • Only for central point(s) of a largeuniformnon-attenuatingdisk source • For FBP-like reconstructions Gain* (standard deviation FWHM) amount of detected coincidences per cm² resolution in reconstructed image (FWHM) * Tomitani, IEEE TNS 28(6), 1981. ** Tanaka et al., Comp. Biol. Med., 1976.

  9. Fisher information-basedevaluation method for TOF-PET • Fisher information covariance matrix of measurement projection backprojection • Efficient approximations for image quality of post-filtered MLEM: • Linearized local impulse response (LLIR) • (Co)variance in 1 pixel or small ROI MLEM post-filter Fessler et al., IEEE TIP 1996.Qi et al., IEEE TMI 2000.Vunckx et al., IEEE TMI 2008. Fixed target resolution

  10. Fisher information-basedevaluation method for TOF-PET Approximations for individual pixels Approximations for ROI Also possible to insert TOF kernel mismatch in model due to local shift invariance pixels in ROI pixel j with r = # pixels in ROI

  11. Outline • Introduction: TOF PET • Aim & motivation • Image quality evaluation methods • Simulations & results • Sim. 1: Tomitani versus Fisher information • Sim. 2: Attenuating ellipse • Sim. 3: Realistic thorax phantom • Sim. 4: Effect accuracy TOF kernel • Conclusions & future work

  12. D= 35.0cm Simulation 1:Tomitani versus Fisher information • Homogeneous diskD = 35 cm • No attenuation, no scatter, no randoms, no detector resolution • Pixel size 0.2 cm • FOV of 67.2 cm • Target resolution 0.6 cm FWHM Nice agreement!

  13. Da = 43.8cm 0.4 Da Db = 28.0cm 0.4 Db Simulation 2:Attenuating ellipse • Homogeneous ellipseDa= 43.8 cm, Db = 28.0 cm • No scatter, no randoms, no detector resolution • Attenuation (water) • Pixel size 0.2 cm • FOV of 67.2 cm • Target resolution 0.6 cm FWHM non-TOF 1.25 17.85 3.37 non-TOF 1.45 1.75 13.9013.73 2.523.31 Gain increases faster in the center!

  14. > 5 4 3 2 1 0 Simulation 3:Realistic thorax phantom True activity • 2D realistic thorax phantom • No scatter, no randoms • Attenuation modeled • Intrinsic resolution 0.5 cm • Pixel size 0.3375 cm • FOV of 64.8 cm • Target resolution 1.2 cm FWHM TOF PET variance Dt = 500 ps Non-TOF PET variance Variance improvement due to TOF information

  15. Simulation 3:Realistic thorax phantom Fisher information-based method Based on recon. of 300 noisy proj. data sets True activity TOF PET variance Dt = 500 ps Non-TOF PET variance > 5 4 Variance improvement due to TOF information 3 2 1 0

  16. D= 27.0cm ROI diam 1.3cm 7cm Simulation 4:Effect accuracy TOF kernel Dt = 300 ps FWHM? • TOF kernel might not be known accurately Effect on image quality? Narrower contrast Wider contrast Daube-Witherspoon, Matej et al., 2006 IEEE NSS Conf. Rec.

  17. D= 27.0cm 7cm ROI diam 1.3cm Simulation 4:Effect accuracy TOF kernel Real TOF kernel Dt = 300 ps Target resolution 6 mm CNR ROI mean ROI variance ROI recon TOF kernel FWHM (ps) recon TOF kernel FWHM (ps) recon TOF kernel FWHM (ps) post-smoothed impulse response post-smoothed impulse response Too narrowDt = 150 ps Too wideDt = 600 ps pixels pixels

  18. D= 27.0cm 7cm ROI diam 1.3cm Simulation 4:Effect accuracy TOF kernel Real TOF kernel Dt = 300 ps Target resolution 6 mm CNR ROI mean ROI variance ROI recon TOF kernel FWHM (ps) recon TOF kernel FWHM (ps) recon TOF kernel FWHM (ps) post-smoothed impulse response post-smoothed impulse response Too narrowDt = 150 ps Too wideDt = 600 ps pixels pixels

  19. Simulation 4:Effect accuracy TOF kernel Reconstructions homogeneous sphere F 27 cm Real TOF kernel Dt = 300 ps TOO NARROW CORRECT TOO WIDE activity Recon TOF kernel FWHM Dt = 150 ps Recon TOF kernel FWHM Dt = 300 ps Recon TOF kernel FWHM Dt = 600 ps pixels (column coordinates)

  20. Simulation 4:Effect accuracy TOF kernel (imposed target FWHM) Gaussian post-filter CNR ROI mean ROI variance ROI recon TOF kernel FWHM (ps) recon TOF kernel FWHM (ps) recon TOF kernel FWHM (ps) Optimal post-filter (imposed Gaussian shape and FWHM) CNR ROI variance ROI mean ROI recon TOF kernel FWHM (ps) recon TOF kernel FWHM (ps) recon TOF kernel FWHM (ps)

  21. Outline • Introduction: TOF PET • Aim & motivation • Image quality evaluation methods • Simulations & results • Conclusions & future work

  22. Conclusions • Gain in center increases with diameter and TOF resolution: • Gain in all pixels • Slower increase in eccentric and high-count regions

  23. Conclusions Gaussian post-filter Optimal post-filter Accurate kernel: best contrast vs.variance trade-off, best CNR Kernel too narrow: contrast contrast = variance variance CNR Kernel too wide: contrast contrast = variance variance CNR artifacts Flat optimum!

  24. Future work • So far: • Design evaluation for multipinhole SPECT • Effect of overlapping projections in multipinhole SPECT • Image quality improvement due to TOF • Also interesting: • Effect of randoms, scatter, … for TOF PET • 3D TOF PET • Rotating slat hole SPECT (Lin Zhou)

More Related