Overview

Pharmacokinetic analysis of concentration time curves is also of interest in the context of dynamic PET acquisition over the accumulation of a radioactive tracer in tissue.

Contact information

This plug-in is being developed by Charlotte Debus and the SIDT group (Software development for Integrated Diagnostics and Therapy) at the German Cancer Research Center (DKFZ). If you have any questions, need support, find a bug or have a feature request, feel free to contact us at www.mitk.org.

Citation information

If you use the view for your research please cite our work as reference:

Debus C and Floca R, Ingrisch M, Kompan I, Maier-Hein K, Abdollahi A, Nolden M, MITK-ModelFit: generic open-source framework for model fits and their exploration in medical imaging – design, implementation and application on the example of DCE-MRI (arXiv:1807.07353)

General information

All models require definition of the arterial tracer concentration, i.e. the AIF. For AIF definition see section 3. Instead of the hematocrit level, the whole blood to plasma correction value needs to be specified. The literature value commonly used is 0.1 Since PET images are already in concentration units of activity per volume ([Bq/ml], translates to number of nuclei per volume), no conversion of signal intensities to concentration is offered in the plugin. If, however, conversion of the 4D images to standard uptake values (SUV) is desired, this can be performed with the separate PET SUV calculation plugin. Start parameters and parameter constraints can be defined in the same manner as for the DCE tool.

Supported models

The PET dynamic plugin works in analogy to the DCE MRI perfusion plugin. It currently supports the following compartmental models:

One tissue compartment model (without blood volume VB)
Extended one tissue compartment model (with blood volume VB)
Two tissue compartment model (with blood volume)
Two tissue compartment model for FDG (without back exchange k4)

Model Settings

Start parameter

In cases of noisy data it can be useful to define the initial starting values of the parameter estimates, at which optimization starts, in order to prevent optimization results in local optima. Each model has default scalar values (applied to every voxel) for initial values of each parameter, however these can be adjusted. Moreover, initial values can also be defined locally for each individual voxel via starting value images.

Constraint settings

To limit the fitting search space and to exclude unphysical/illogical results for model parameter estimates, constraints to individual parameters as well as combinations can be imposed. Each model has default constraints, however, new ones can be defined or removed by the + and – buttons in the table. The first column specifies the parameter(s) involved in the constraint (if multiple selected, their sum will be used) by selection in the drop down menu. The second column defines whether the constraints defines an upper or lower boundary. Value and Width define the actual constraint value, that should not be crossed, and a certain tolerance width.

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