Model calculations for PET images
Robust analysis techniques can be applied to dynamic PET images, producing parametric images.
Common pixelbypixel analysis methods
Models for reversible tracer uptake
Outcome is the volume of distribution, binding potential, or a related parameter.
Without blood sampling
 Simplified reference tissue model (SRTM) can be used to compute BP and R_{1} images, using PMOD, or TPC software applying basis function approach (with imgbfbp) or multilinear model approach, solved with NNLS (using imgsrtm); the latter method is sensitive to noise, and thus smoothing would be recommended
 Logan analysis to produce DVR image (sensitive to noise, smoothing recommended)
 Latescan 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 plasma input
 Kinetic model fit to produce volume of distribution image (sensitive to noise, smoothing recommended)
 Logan analysis to produce volume of distribution image (sensitive to noise, smoothing recommended)
 Latescan tissue/plasma ratio: may correlate with the volume of distribution
 Reversible onetissue model can be used to compute K_{1} and k_{2} or K_{1}/k_{2} images using either basis function approach (with imgbfk2) or multilinear model approach (with imglhk1).
Models for irreversible tracer uptake
Outcome is the K_{i}, FUR, k_{3}, or a related parameter, representing metabolic rate, or enzyme or transporter activity.
Without blood sampling
 Patlak analysis to produce parametric K_{i} image
 Standardized uptake value (SUV) image
 TRTM can be applied to compute k_{3} image using program imgtrtm if a positive reference region TAC is available.
With metabolite corrected plasma input
 Patlak analysis to produce parametric K_{i} image (e.g. glucose uptake)
 Fractional uptake rate (FUR) image
 Latescan tissue/plasma ratio: may correlate with Ki
 2 or 3CM fit to produce K_{i} image
 3CM fit to produce K_{1} and k_{3} image, or λ*k_{3} image with multilinear fit or with Fowler & Logan method
Perfusion (blood flow)
Without blood sampling
 Earlyscan tissue/reference tissue ratio (may correlate with the patterns of blood flow)
With arterial blood sampling
 Autoradiography (ARG) method for bolus [^{15}O]H_{2}OPET studies
 Compartment model for bolus [^{15}O]H_{2}OPET studies using either basis function approach (with imgbfh2o) or multilinear model approach (with imgflow).
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
 Create SIF 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
See also:
 Cardiac image analysis system
 Instruction by tracer
 Input function
 Correlation coefficient constrained parametric images
 Model calculations for sinograms
 Tools for processing image data
 Parametric images in presentations and reports
 Multilinear models
Literature
Bentourkia M. A flexible image segmentation prior to parametric estimation. Comput Med Imaging Graph. 2001; 25: 501506. doi: 10.1016/S08956111(01)000167.
Feng DD, Wen L, Eberl S. Techniques for parametric imaging. In: Feng DD (ed.): Biomedical Information Technology. Elsevier, 2008, pp 137163. ISBN: 9780080550725. doi: 10.1016/B9780123735836.500104.
Herholz K. Nonstationary spatial filtering and accelerated curve fitting for parametric imaging with dynamic PET. Eur J Nucl Med. 1988; 14: 477484. doi: 10.1007/BF00252392.
Zhou Y, Huang SC, Bergsneider M, Wong DF. Improved parametric image generation using spatialtemporal analysis of dynamic PET studies. Neuroimage 2002; 15(3): 697707. doi: 10.1006/nimg.2001.1021.
Tags: Image, Modeling, Analysis, NNLS, SRTM
Updated at: 20230616
Created at: 20080118
Written by: Vesa Oikonen, Kaisa Liukko