Fitting functions to PET TTACs
Mathematical functions or compartment models can be fitted to regional tissue time-activity curves (TTACs). Fitting functions to TTACs is usually not necessary, and should be avoided as a pre-processing step before compartmental modelling. Instead, Input functions and plasma parent fractions frequently need to be fitted in order to enable interpolation and extrapolation or to reduce noise. Noise may be a problem with TTACs when assessing TTAC peak value, for example for the purpose of estimating binding potential using transient equilibrium method, and fitting an empirical function to the TTACs may help to determine the peak values without noise-induced bias and variance. Another possible use is to estimate the tracer appearance time for time delay correction, or accounting for the time delay in AUC calculation for ARG methods.
Feng et al. (1993) proposed a set of functions for fitting input curves, and these functions can also be used to fit TTACs. The “model 2” is a combination of the surge function and exponentials:
, and can be fitted to PET TTACs with program fit_feng.
Rational functions (ratio of polynomials) can well represent the regional tissue data, but are not suitable for extrapolation because of the discontinuities at the zeroes of the divider function. Rational functions can be fitted to TTACs using fit_ratf.
, can be fitted to PET TTACs with program fit_wcdf.
Simple sums of exponential functions have been traditionally used to fit not only input curves but also regional TTACs. If bolus injection is administered into local tissue artery, then tracer washout curve can be well fitted with decreasing exponentials. Program fit_exp can be used for these purposes.
TTAC samples can be weighted by 1 / frame length to prevent overfitting the initial part with shorter frames.
Using the fits
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Feng D, Wang Z. A three-stage parameter estimation algorithm for tracer concentration kinetic modelling with positron emission tomography. Proceedings, 1991 American Control Conference, vol 2 (1991): 1404-1405.
Motulsky HJ, Ransnas LA. Fitting curves to data using nonlinear regression: a practical and nonmathematical review. FASEB J. 1987; 1: 365-374. doi: 10.1096/fasebj.1.5.3315805.
Muzic RF Jr, Christian BT. Evaluation of objective functions for estimation of kinetic parameters. Med Phys. 2006; 32(2): 342-353. doi: 10.1118/1.2135907.
Young P: Everything You Wanted to Know About Data Analysis and Fitting but Were Afraid to Ask. Springer, 2005. ISBN 978-3-319-19051-8.
Created at: 2018-05-17
Updated at: 2018-05-17
Written by: Vesa Oikonen