1. Data Loading

 Create a new data set.
 Load the plasma curve (total activity concentration of tracer in the plasma fraction of the blood samples).
 Load the parent fraction curve (fraction of unchanged tracer in plasma, optional).
 Load the whole blood curve (total activity concentration of tracer in the blood samples, optional).
 Load the average timeactivity curves of one or multiple tissue regions.

2. Input Curve Configuration

 Define interpolation functions (called models) for the different types of blood data and fit them to the data.
 The input curve is obtained as the multiplication of the plasma model with the parent fraction model.

3. Kinetic Model Configuration

 Select a region with a "typical" timeactivity curve (TAC) from the Region list.
 Select a simple model from the Model list.
 Enable the parameters to be fitted by checking their boxes; unchecked parameters remain at the initial values entered.
 Select a weighting scheme of the residuals; variable or constant (default) weighting is available.

4. Kinetic Model Fitting

 Fit the model parameters by activating the Fit current region button.
 Consult the residuals to check whether the model is adequate; there should ideally be no bias in the residuals, just random noise.
 If the model is fine, configure Copy the model to all regions to Model & Par, activate the button to establish then save initial model configuration for all TACs, then activate Fit all regions.
 If the model is not yet fine, test more complex models.

5. Kinetic Model Comparison

 Switch between compartment models of different complexity and fit. The parameters are either maintained for each model type, or converted, according to the Model conversion setting in the Menu.
 Check the residuals for judging model adequacy.
 Check the different criteria on the Details tab (Schwartz Criterion SC, Akaike Information Criterion AIC, Model Selection Criterion MSC) to decide whether a more complex model is supported by the data.
 Check for parameter identifiability. As an indicator of the parameter standard error coefficients of variation (%COV) are returned from the fit. They should remain limited for all relevant parameters. Additionally, Monte Carlo simulations can be performed to obtain statistics of the parameter estimates.
 If justified by physiology, try to improve the stability of parameter estimation by enforcing common parameters among regions in a coupled fitting procedure.
 Compare the outcome of compartment models with that of other models, such as reference models or graphical plots.

6. Saving

 Save all model information together with the data in a composite text .km file which allows restoring a session.
 Save a summary of all regional parameters in an EXCELready text file .kinPar.

7. Batch Processing

Lengthy calculations such as coupled fits with many regional TACs or Monte Carlo simulations can be run in a batch job. The results are saved both as .km files and in an EXCELready text file.
