Home < Documentation < 4.3 < Modules < PkModeling
Introduction and Acknowledgements
This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, and by National Cancer Institute as part of the Quantitative Imaging Network initiative (U01CA151261).
Implementation of the pharmacokinetics modeling was contributed by Yingxuan Zhu and Jim Miller from GE Research.
Author: Yingxuan Zhu (while at GE), Andrey Fedorov (SPL), Jim Miller (GE)
Contact: Jim Miller, <email>firstname.lastname@example.org</email>
GE Global Research
National Alliance for Medical Image Computing (NA-MIC)
PkModeling (Pharmacokinetics Modeling) calculates quantitative parameters from Dynamic Contrast Enhanced DCE-MRI images. This module performs two operations:
- Converts signal intensities to concentration values. The concentration values are used to calculate quantitative parameters.
- Calculates quantitative parameters from concentration values. These parameters include:
- Volume transfer constant between blood plasma and EES (extracellular-extravascular space) at each voxel (min^-1)
- Fractional volume for extracellular space at each voxel
- Maximum slope in the time series curve of each voxel
- Area under the curve of each voxel, measured from bolus arrival time to the end time of interval, normalized by the AUC of the AIF
Panels and their use
- PkModeling Parameters:
- T1 Blood Value (milliseconds)
- T1 Tissue Value (milliseconds) (default value is the published value for prostate tissue estimated in healthy individuals (see Ref. de Bazelaire et al.)
- r1 Relaxivity Value of the contrast agent, L x mol^(-1) x s^(-1). This value is contrast agent specific. Default setting of 0.0039 corresponds to Gd-DPTA (Magnevist) at 3T, see Ref. Pintaske et al. You will need to adjust this setting based on the magnet signal strength and contrast agent.
- Hematocrit Value. Volume percentage of red blood cells in blood.
- AUC Time Interval Value: Time interval for AUC calculation
- Input 4D Image: 4D DCE-MRI data
- AIF Mask Image: Mask designating the location of the arterial input function (AIF). AIF can either be calculated from the input using the aifMask or can be prescribed directly in concentration units using the prescribedAIF option.
- Prescribed AIF: Prescribed arterial input function (AIF). AIF can either be calculated from the input using the aifMask option or can be prescribed directly in concentration units using the prescribedAIF option.
- Output Ktrans image
- Output ve image
- Output fpv image
- Output maximum slope image
- Output AUC image
- Advanced options:
- Output R-squared goodness of fit image: each pixel will be initialized to a value between 0 and 1 characterizing the goodness of fit. Larger values correspond to a better fit (see R^2 measure description)
The following acquisition parameters should be available in the NRRD header of the input data (if you are analyzing a DICOM time series, they will typically be extracted from the DICOM data):
- TR Value: Repetition time (milliseconds)
- TE Value: Echo time (milliseconds)
- FA Value: Flip angle (degrees)
- Timestamps for the dynamic series (in milliseconds)
Here is an example how this information is represented in the NRRD header:
-  Knopp MV, Giesel FL, Marcos H et al: Dynamic contrast-enhanced magnetic resonance imaging in oncology. Top Magn Reson Imaging, 2001; 12:301-308.
-  Rijpkema M, Kaanders JHAM, Joosten FBM et al: Method for quantitative mapping of dynamic MRI contrast agent uptake in human tumors. J Magn Reson Imaging 2001; 14:457-463.
-  de Bazelaire, C.M., et al., MR imaging relaxation times of abdominal and pelvic tissues measured in vivo at 3.0 T: preliminary results. Radiology, 2004. 230(3): p. 652-9.
-  Pintaske J, Martirosian P, Graf H, Erb G, Lodemann K-P, Claussen CD, Schick F. Relaxivity of Gadopentetate Dimeglumine (Magnevist), Gadobutrol (Gadovist), and Gadobenate Dimeglumine (MultiHance) in human blood plasma at 0.2, 1.5, and 3 Tesla. Investigative radiology. 2006 March;41(3):213–21.
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