Models for simulation of regional PET TTACs

Software for simulation

PET tissue time-activity curves (TTACs) can be simulated based on the input function, compartmental model, and model parameters, using the software listed below:

Compartmental models with plasma input

• 3-tissue compartment model: sim_3tcm for simulating optionally more than one TTACs at the same time, with parameters in a text file;
  or p2t_3c and p2t_v3c for simulating one TTAC with parameters on command-line.
• 4-tissue compartment model with dual-input and/or k_Loss

Models with 1- or 2-tissue compartments can be simulated simply by setting the rate constants that apply to the second or third compartment to zero.

For demonstration purposes Excel worksheets are available for the simulation of

• 2-TCM
• 3-TCM with parallel compartments
• 3-TCM with compartments in series

These programs and worksheets use the direct ODE solutions for discrete-time data. There really is no reason to use the convolution method for simulations, but programs and convexpf are available for that purpose.

2-tissue compartment model with reference tissue input

Program sim_rtcm can be used to simulate TTACs using the following reference tissue input models:

• Full reference tissue model
• Simplified reference tissue model
• Reduced reference tissue model (k₄=0)
• Transport limited reference tissue model

Dedicated models for PET tracers or targets

• [¹⁵O]H₂O in myocardium
• [¹⁵O]H₂O in liver
• General [¹⁵O]H₂O model: b2t_h2o or sim_h2o
• [¹⁵O]O₂ in skeletal muscle
• [¹⁸F]FDOPA (outdated)
• [¹⁸F]FETNIM (outdated)

Shape analysis

Program simshape can be used to simulate regional TTAC using shape analysis method.

See also:

• Parameters for simulation
• Input for simulations
• Adding noise to simulated data
• Compartmental models
• Analysis of regional TAC data

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Literature

Chen K, Huang S, Feng D. New estimation methods that directly use the time accumulated counts in the input function in quantitative dynamic PET studies. Phys Med Biol. 1994; 39: 2073-2090. doi: 10.1088/0031-9155/39/11/017.

Coxson PG, Salmeron EM, Huesman RH, Mazoyer BM. Simulation of compartmental models for kinetic data from a positron emission tomograph. Comput Methods Progr Biomed. 1992; 37: 205-214. doi: 10.1016/0169-2607(92)90116-O.

Coxson PG, Huesman RH, Borland L. Consequences of using a simplified kinetic model for dynamic PET data. J Nucl Med. 1997; 38: 660-667. PMID: 9098221.

Huang S, Bahn MM, Phelps ME. A new technique for solving nonlinear differential equations encountered in modeling of neuroreceptor-binding ligands. IEEE Trans Nucl Sci. 1988; 35(1): 762-766. doi: 10.1109/23.12828.

Kuwabara H, Cumming P, Reith J, Léger G, Diksic M, Evans AC, Gjedde A. Human striatal L-DOPA decarboxylase activity estimated in vivo using 6[¹⁸F]fluoro-DOPA and positron emission tomography: error analysis and application to normal subjects. J Cereb Blood Flow Metab. 1993; 13: 43-56. doi: 10.1038/jcbfm.1993.7.

Tags: Simulation, TAC, Modeling, Excel, Compartmental model, Rate constant, Parameters, 1TCM, 2TCM, 3TCM

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Updated at: 2019-10-25
Created at: 2010-09-20
Written by: Vesa Oikonen