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Whole Blood and Plasma Activity

The interpolation of the activity curves of whole blood and plasma are performed in the same way.

Whole Blood Model Configuration

It is assumed that the time-course of the tracer activity in whole blood has been loaded with Menu/Load Whole Blood. To configure the interpolation model of whole blood please select the Blood tab, and set the Whole blood radio button. A list of models is available which can be shown with the arrow button indicated below.

PKIN Blood Selection

Default is Measured, which just represents linear interpolation between sample times. To replace linear interpolation by a smoother function select an appropriate definition from the list. As soon as a model is selected, the parameters are updated in the Standard pane, and a corresponding model curve is shown in the curve window as Whole blood model.

Input Curve Model

In the example above the model has not yet been fitted so that the distinction between the measurement and the model (sum of exponentials) is clearly visible. The parameters of the blood model can be configured for fitting purposes by enabling the check boxes. Activating the Fit Whole Blood button starts a fitting process which adjusts the model parameters such that the interpolation curve comes into optimal agreement with the measurements. Fitting works exactly in the same way as explained for the tissue model below.

Plasma Activity Model Configuration

It is assumed that the time-course of the tracer activity in the arterial plasma has been loaded with Menu/Load Plasma Activity. The configuration of the interpolation model works in exactly the same way as for whole blood.

Please select the Blood tab, and set the Plasma radio button. Note the label Plasma activity which indicates that the current working mode uses measured activities, not a derivation from whole blood activity. The Clear activity data button is available to change this mode. If it is activated, the loaded plasma activity data is discarded and the mode of plasma switched to plasma fraction. In that case the assumption is that an analytical plasma fraction function will be applied to the whole blood activity curve to derive the plasma activity.

The same list of models is available as for the plasma activity as for whole blood.

Available Whole Blood and Plasma Activity Models

PKIN provides the following choice of interpolation functions for blood or plasma activity.

Measured:

This is the default model, whereby the input curve is linearly interpolated between measured values. Outside the measured values the interpolation rules are as follows: The input curve is

3 Exponentials:

In this model the tail of the input curve from a starting point to be specified is replaced by a sum of up to three exponentials. The input curve before the start time is linearly interpolated as for the measured model.

Compartment Model, 3 Eigenvalues:

This model has been developed for the FDG tracer [14]. It is the result from modeling the distribution and delivery of FDG in the circulatory system by a compartment model, and is given by

Compartment Model, 2 Eigenvalues:

This is a reduced version of the above model given by

Metabolite correction (deprecated):

This model has been developed for correcting the buildup of the CO2 metabolite in 11C acetate studies [22]. The model assumes that the concentration of authentic ligand in plasma can be calculated from total blood activity by the following simple equation

where met denotes the metabolite level which is finally reached, and T represents the half-time of the exponential metabolite build-up. This model can be applied for other tracers with a similar behavior of metabolite buildup.

Note: the Metabolite correction model can not be fitted against the blood data. One way to use this metabolite correction is to determine the two parameters externally and enter them as constants in the model. An alternative is to estimate the metabolite parameters together with the kinetic parameters during a fit of the kinetic model (with the Fit blood parameters check enabled).

Bolus/Infusion optimization:

This model has been developed for the optimization of the activity ratio between an initial bolus and a subsequent infusion [23]. The aim is, that the activity level in plasma and in the tissue gets constant as soon as possible.

Multiinjection, HOT or COLD:

These models are used in combination with the model for multi-injection studies with 11C-Flumazenil as described by Delforge et al. [26]. With the HOT model, there is an analytical correction for the buildup of metabolites included. The COLD model is entirely derived from the hot input curve based on the relative doses and includes similar metabolite correction.

The Blood Delay Parameter

All standard blood models have a Delay parameter to correct for a timing error between tissue and blood data. Positive delays represent delayed blood information and hence shift the blood curves to earlier times (to the left). This parameter is only relevant for fitting of the kinetic model. Therefore, when fitting the shape of the blood curve with Fit plasma or Fit whole blood, it is automatically disabled.

However, when fitting Tissue compartment models, it is possible to fit the blood delay as an additional parameter to the parameters of the kinetic model.

Blood delay fitting

  1. On the Blood tab, select Plasma, and then check the fitting box of the Delay parameter.
  2. Disable the fit boxes of all parameters except Delay in all blood models
  3. On the Tissue tab, check the Fit blood param box.
  4. Then activate Fit current region the Tissue tab.

To find out the fitted blood delay, select the Blood tab after fitting. The fit value is shown, and the input curve model appears shifted accordingly.

CAUTION: When Fit blood param is enabled, all checked parameters of the input curve model are fitted. So if other parameters than Delay are also enabled, the shape of the input curve will also change!

Default Values of Model Parameters

Each model has default values. Initially, they are factory settings, but they can be re-defined by the user if he wants to establish a default configurations which is more adequate from him. Note that the configurations are valid per PMOD login, so different logins could be prepared for processing types of data requiring different initial parameter values.

Saves the current configuration of the model as the new default. Included are the values, the fit flags, and the restrictions of all parameters.

Retrieves the default configuration of the model.