# Exponential functions as PET model input

Exponential function has a relationship with compartmental models. In pharmacokinetic one-compartment model drug clearance can be estimated by fitting a single exponential function to the decreasing curve of the drug plasma concentration. Sum of exponential functions can be used to describe the kinetics of models with more compartments.

Sums of exponential functions (Figure 1) can also be used to fit the decreasing tail of bolus plasma or blood curves collected during a PET study for estimating the plasma pharmacokinetics of the radiotracer.

Monoexponential extrapolation of tracer clearance curves is a common procedure (Lassen & Sejrsen, 1971). For extrapolating the input curve, extrapol can be used. However, better approach usually is to write a script which first fits 2 or 3 exponential function (with fit_exp) to the input data starting at some time after the peak is passed, then computes the fitted TAC at desired time points using fit2dat, and finally combines the original TAC and fitted tail using dftcat.

Arterial curves after bolus injection can be very concave, and decent fitting requires
*N≥3*. Instead of that, residence times can be approximated by *gamma distribution*,
which can be interpreted in terms of recirculatory pharmacokinetic model
(Weiss, 1983), or with a form of *Weibull
distribution* (Piotrovskii, 1987a,
1987b), using either of the functions

instead of one of the exponential terms. In these functions *0<β<1*. By
setting *β* to zero or one, respectively, the functions become normal exponentials.

Actual arterial PET input curves, following intravenous bolus
administration of radiotracer, typically start with a period of
zero concentration because of the delay that it takes for the bolus to reach the sampling site.
After that follows a steep rise, then the concentration curve passes through a maximum, and then
undergo a slower decay. While the descending part of the bolus curve can be fitted using the sum of
decaying exponentials, the ascending phase (before peak time, *T _{peak}*) can be
fitted using a line (Parsey et al., 2005;
Vriens et al., 2009):

, or, assuming that ascending phase starts at time 0 (Su et al., 1994):

Ascending phase has also been fitted using a 2nd or 3rd order polynomial function (Eberl et al., 1997; Bentourkia et al., 1999), which however can lead to negative function values. Non-negativity can be assured by fitting the ascending phase using instead function (Wong et al., 2006):

Based on a compartmentalized model of tracer behaviour in the circulatory system, Feng et al.
(1993a and
1993b) have proposed a formulation of
four exponential functions for fitting PET radiotracer PTACs which include both ascending and
descending phase in the measured data,
and the initial zero phase before the tracer appearance time, *T _{ap}*:

Eigenvalues (*λ _{1}*-

*λ*) are ≥0. Examples of curves based on that function are shown in Figure 2. Functions derivative is

_{4}To ensure that the function does not give negative (non-physiological) values at the start,
that is, *f’(T _{ap})>0*, this condition has to be met:

Two of the eigenvalues can be paired, leading to this function.

Fitting a multiexponential function is an ill-posed problem and not that easy to solve as it might first appear (Istratov & Vyvenko, 1999). The parameters of the descending exponential part can be estimated with spectral analysis (Bentourkia et al., 1999).

Especially in DCE-MRI studies, but also in PET field, “biexponential” may refer to the sum of two
exponentials (McGrath et al., 2009), a function
presented above with *N=2*, or, “biexponential AIF model”, which is
actually a compartmental model used to calculate realistic
BTAC from a rectangular (boxcar) function that represents the infusion of the contrast agent or
radiotracer.

## Infusion

To simulate the radiotracer infusion, of duration *T _{in}*, the boxcar function
can be convolved with the sum of exponentials function
(Eq 1).
TAC can be convolved with sum of exponentials using convexpf.
Convolution operation can here be replaced by subtracting integrals of the exponentials.
The integral of exponential function is

and the subtraction is calculated as

The function can be calculated in three parts:

In pharmacokinetics and simulations the plasma concentration during and after intravenous
infusion have been described using this (or with notation differences) function
(Wagner, 1976;
Madsen et al., 1993;
Oakes et al., 1999).
This function, optionally with a few extensions, can be fitted with program
fit_sinf.
*T _{ap}* is the appearance time of radioactivity in the blood or plasma, and

*T*is the infusion time.

_{in}*N*should usually be set to 2 or 3, but in some situations higher number of parameters is required.

The integrals of the same exponential functions that were shown in Figure 1
are shown in Figure 3, together with the infusion model calculated with
*T _{ap}=0* and

*T*. As can be seen from the plots, the function models well the initial delay and dispersion that happens when the bolus travels from IV injection site through the heart cavities and lungs to the arterial system, and the sum function can basically handle the recirculation and redistribution. However, the measured arterial curves are further dispersed in the circulation, and unless curve is dispersion corrected, it cannot be well fitted with this function. If dispersion time constant (

_{in}=100*τ*) is known, then the effect of dispersion can be added to the function in fit_sinf; this helps to get markedly better fits, and allows noise-free dispersion correction.

## Recirculation

The integral of exponential function has been often used for describing the recirculation phase of the plasma TACs in pharmacokinetics and also in PET. The plasma curve is fitted to a function that is the sum of exponential function and its integral

, where *c* is a scaling factor.
The exponential functions that were shown in Figure 1 and their scaled integrals
(Figure 3, left side) are summed and shown in Figure 4.
Similar and more versatile curve shapes can be achieved without the integral function if more
exponentials are added, but that increases also the number of function parameters which complicates
the fitting.
The same approach can be applied to any function.

## See also:

- Fitting input function
- Compartmental models for input function
- Blood sampling
- Input function
- Time-delay
- Dispersion
- Input for simulations
- Fitting compartmental models
- Fitting TTACs
- Plasma pharmacokinetics
- Area under curve (AUC)
- Integral Calculator
- Derivative Calculator

## References

Feng D, Huang S-C, Wang X. Models for computer simulation studies of input functions for
tracer kinetic modeling with positron emission tomography.
*Int J Biomed Comput.* 1993; 32: 95-110.
doi: 10.1016/0020-7101(93)90049-C.

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.
doi: 10.23919/ACC.1991.4791609.

Lüdemann L, Sreenivasa G, Michel R, Rosner C, Plotkin M, Felix R, Wust P, Amthauer H.
Corrections of arterial input function for dynamic H_{2}^{15}O PET to assess
perfusion of pelvic tumours: arterial blood sampling versus image extraction.
*Phys Med Biol.* 2006; 51: 2883-2900.
doi: 10.1088/0031-9155/51/11/014.

Wagner JG. Linear pharmacokinetic equations allowing direct calculation of many needed
pharmacokinetic parameters from the coefficients and exponents of polyexponential equations which
have been fitted to the data. *J Pharmacokin Biopharm.* 1976; 4(5): 443-467.
doi: 10.1007/BF01062831.

Tags: Input function, Fitting, Extrapolation, Exponential function, Biexponential

Updated at: 2019-02-15

Created at: 2016-08-08

Written by: Vesa Oikonen