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Model calculation for PET images

General pixel-by-pixel analysis methods for PET images

Reversible models (Distribution volume and Receptor binding potential)
- Without blood sampling
  - Simplified reference tissue model (SRTM) to produce BP and R₁ images
    - Multilinear model approach to SRTM solved with NNLS
      (sensitive to noise, smoothing recommended)
    - Basis function approach to SRTM
  - Logan analysis to produce DVR image (sensitive to noise, smoothing recommended)
  - Late-scan tissue/reference tissue ratio (equals DVR in bolus+infusion studies)
  - AUC tissue/reference tissue ratio (may correlate with BP or DVR)
  - Standardized uptake value (SUV) image
- With metabolite corrected blood curve
  - Kinetic model fit to produce DV image (sensitive to noise, smoothing recommended)
  - Logan analysis to produce DV image (sensitive to noise, smoothing recommended)
  - Late-scan tissue/plasma ratio: may correlate with DV
Irreversible models (Enzyme or transporter activity)
- Without blood sampling
  - Gjedde-Patlak analysis to produce parametric K_i image
  - Standardized uptake value (SUV) image
- With metabolite corrected blood curve
  - Gjedde-Patlak analysis to produce parametric K_i image (e.g. glucose uptake)
  - Fractional uptake rate (FUR) image
  - 2- or 3-CM fit to produce K_i image
  - 3-CM fit to produce K₁ and k₃ image, or lambda*k3 image with multilinear fit or with Fowler & Logan method
Perfusion (blood flow)
- Without blood sampling
  - Early-scan tissue/reference tissue ratio (may correlate with the patterns of blood flow)
- With arterial blood sampling
  - Autoradiography (ARG) method for bolus [¹⁵O]H₂O-PET studies
  - Compartment model for bolus [¹⁵O]H₂O-PET studies
Cardiac image analysis system

Detailed procedures for computation of parametric images:

List by tracer

Miscellaneous utility software for image model calculation

Correction of vascular blood radioactivity in dynamic image
Subtraction of reference region TAC from a dynamic image
Thresholding and reducing image noise.
"Clustering" dynamic images, based on method suggested by M'hamed Bentourkia (2001).
Simple arithmetic calculation for ECAT sinogram and image files
Make SIF file from PET image or sinogram or DFT file
Correlation (linear regression) between two parametric images (same head, different analysis method), or
residual image (same patient, before and after stimulus): imgcorrl with option -resid