This is a standard receptor-ligand model with two tissue compartments defined by
C1 represents free and non- specifically bound tracer in tissue, and C2 tracer bound to the receptors. The model accounts for the saturation of receptor sites due to a low specific activity Sact of the injected tracer. In this situation, k3 is given by kon(Bmax-C2/Sact), and hence is time-dependent. Bmax is the total receptor concentration, kon the bi-molecular association rate, and k4 the dissociation rate constant koff.
System of differential equations :
As described above the 2-tissue compartment model can be described by the linear arrangement of compartments
and the system of differential equations:
Given the input curve Cplasma(t) and a set of model parameters K1, .. , k4, the tissue concentrations C1(t) and C2(t) can be calculated by integration of the equation system. However, alternative solutions of the system are possible. With linearized solutions, the equations are integrated twice on both sides, substitutions performed and finally rearranged. This can be done in different ways. The Linear Least Squares method implemented in PKIN uses the derivation of Cai et al. , equation (6):
Note that the tissue curve CPET(t) which is the target function appears on both sides of the equation. It also includes whole-blood activity CBlood(t) for spillover correction as well as the input curve CP(t). This multi-linear expression can be solved in a least squares sense in one step without iterations. The present implementation uses a singular value decomposition method. As a result the 5 parameters P1, .. , P5 are obtained from which the target parameters can be calculated as follows: , equation (9)
Recommended Use of the Linear Least Squares Method
aThe advantage of the linearized approach is the fast calculation, a well-defined solution, and no danger to get stuck in a local minimum such as the iterative methods. However, it is well known that it is susceptible to bias, and that small TAC perturbations can cause large changes of the parameter estimates. Therefore it is recommended that the Linear Least Squares method is only used for getting a quick solution which is further refined by the iterative methods. In fact, if Parameters Initialization is configured in Extras, the initial starting parameters of the compartment models are obtained by the Linear Least Squares method.
The Linear Least Squares method can be used for different model configurations: