1 / 29

GENERATION OF PARAMETRIC IMAGES

GENERATION OF PARAMETRIC IMAGES. PROSPECTS PROBLEMS. Vesa Oikonen Turku PET Centre 2004-03-25. Multiple-Time Graphical Analysis. Gjedde-Patlak plot for irreversible uptake Logan plot for reversible uptake Independent on model structure Plasma and reference tissue input Fast computation

marisa
Download Presentation

GENERATION OF PARAMETRIC IMAGES

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. GENERATION OF PARAMETRIC IMAGES • PROSPECTS • PROBLEMS Vesa Oikonen Turku PET Centre 2004-03-25

  2. Multiple-Time Graphical Analysis • Gjedde-Patlak plot for irreversible uptake • Logan plot for reversible uptake • Independent on model structure • Plasma and reference tissue input • Fast computation • Available everywhere

  3. Multiple-Time Graphical Analysis • Regional analysis to determine the time when plot becomes linear • For Logan analysis with reference tissue input: compartment model fit to determine population average of reference tissue k2

  4. Multiple-Time Graphical Analysis • Examples of DV and DVR images

  5. Multiple-Time Graphical Analysis • Different regions have different kinetics • Usually linear phase is reached later in regions of high uptake • Solution: select fit range separately for each pixel

  6. Multiple-Time Graphical Analysis DVR image where fit range was determined separately for each pixel Nr of frames used in line fit(darker=more frames

  7. Compartmental model fit • Also time before equilibrium is used in the fit • Parametersare solvedfrommultilinearequations

  8. Compartmental model fit • Fast computation from multilinear equations with standard techniques • Multilinear equations can be transformed to solve macroparameters (DV or Ki) without division

  9. Compartmental model fit DV

  10. Compartmental model fit • Pixel-by-pixel selection between 2CM and 3CM based on Akaike Information Criteria (AIC), or • Akaike weighted average of DV from 2CM and 3CM fits (Turkheimer et al 2003)

  11. Compartmental model fit Relative weights of2CM (white) and 3CM (black)based on AIC Akaike weighted average ofDV from 2CM and 3CM

  12. Compartmental model fit • Alternative to Akaike weighting:Lawson-Hanson non-negative least-squares (NNLS) produces good-quality DV and DVR images from multilinear 3CM

  13. Simplified Reference Tissue Method (SRTM) • Basis Function Method (BFM) • Multilinear equations Binding Potential (BP) solved using

  14. SRTM-BFM • Parameter bounds must be determined based on regional analysis • Tight bounds cause poor fit and bias in some regions • Wide bounds may lead to long-tailed BP distribution and positive bias

  15. SRTM-NNLS • Multilinear equation can be transformed to solve BP+1 without division • Provides good-quality BP images when NNLS is used

  16. SRTM-NNLS BP image calculatedusing SRTM-NNLS

  17. Parametric sinogram • Faster ( iterative ) reconstruction • Intrinsic ”heterogeneity” • All linear models applicable

  18. Parametric sinogram DVR sinogram DVR imageFBP reconstruction

  19. Calculation on sinogram level • Correct for physical decay • Correct for frame lengths • Model calculation as usual • Reconstruction • Divide pixel values by volume (if not done in reconstruction or calibration) • Calibration (only with plasma input, and not even then for all parameters) • Calculations with parameters after reconstruction

  20. Parametric sinogram: problems • Multiple-Time Graphical Analysis: when linear phase starts? • Multilinear equations: which model? • Reference input TAC: pre-reconstruction needed

  21. Parametric sinogram:more problems • Dynamic sinogram must be filtered before calculation; avoid another filtering in reconstruction! • Requires full knowledge on raw data collection and processing steps

  22. Parametric sinogram • In future:Iterative reconstruction and model calculation combined

  23. PROBLEMS • Noise induses bias in all linear methods for reversible uptake • Logan plot: no satisfactory method for removing bias • Multilinear methods: GLLS can not be applied to reference tissue models

  24. More problems • SRTM can not be used for all tracers • Weights for fitting are not known • Partial volume error (PVE)may lead to artefactual second tissue compartment in reference region

  25. More problems • Movement during scanning DVR image without movement and after moving 3 frames 4 mm (2 pxls) upward

  26. Movement during scanning • Complicated models are more sensitive to movement Same simulation, but Logan plot computed with variable line fit start time

  27. Image filtering • Only working method to reduce bias in linear models • Resolution need not to be preserved if next analysis step is SPM or other brain averaging method • Biases may be cancelled out in calculation of occupancy maps

  28. Cluster analysis • Resolution preserving smoothing for dynamic images • Automatic extraction of reference tissue curve • Extraction of curves with different kinetics: Validation that selected model can fit them all

  29. CONCLUSION • Problems: image noise, patient movement and inconsistent input data • Until solved, use only simple models causing biases but less artefacts • Validation in animal models and in vitro is essential

More Related