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PKModellingPy is a software tool written in Python for pharmacokinetic (PK) modeling of dynamic contrast enhanced MRI (DCE MRI) based on Tofts Model [1][2]. The software is similar to the opensource 3D Slicer CLI extension PkModeling.

While 3D Slicer PKModelling extension is a powerful software which have access to several visualization and image processing tools in Slicer, PKModellingPy can be useful for fast prototyping and testing of new algorithms in Python. PK Modelling internals can be investigated easily with PKModellingPy specifically in the interactive enviornment of IPython. This provides a valuable tool for troubleshooting and fast implementation of new methods and algorithms. The functional tools that has been tested in PKModellingPy will be eventually ported to the 3D Slicer PKModelling extension.

PKModellingPY Notebooks

Usage Examples and Screenshots

References

DCE-MRI Data

Literature

DCE-MRI PK Modelling

[1] Paul S. Tofts, PhD. "T1-weighted DCE Imaging Concepts: Modelling, Acquisition and Analysis". [2] Tofts, P S, and A G Kermode. 1991. “Measurement of the Blood-Brain Barrier Permeability and Leakage Space Using Dynamic MR Imaging. 1. Fundamental Concepts.” Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 17 (2): 357–67. [3] Tofts, P S, G Brix, D L Buckley, J L Evelhoch, E Henderson, M V Knopp, H B Larsson, et al. 1999. “Estimating Kinetic Parameters from Dynamic Contrast-Enhanced T(1)-Weighted MRI of a Diffusable Tracer: Standardized Quantities and Symbols.” Journal of Magnetic Resonance Imaging: JMRI 10 (3): 223–32.

Reproducibility of PK Metrics [4] Galbraith, Susan M, Martin A Lodge, N Jane Taylor, Gordon J S Rustin, Søren Bentzen, J James Stirling, and Anwar R Padhani. 2002. “Reproducibility of Dynamic Contrast-Enhanced MRI in Human Muscle and Tumours: Comparison of Quantitative and Semi-Quantitative Analysis.” NMR in Biomedicine 15 (2): 132–42. [5] Huang, Wei, Xin Li, Yiyi Chen, Xia Li, Ming-Ching Chang, Matthew J Oborski, Dariya I Malyarenko, et al. 2014. “Variations of Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Evaluation of Breast Cancer Therapy Response: A Multicenter Data Analysis Challenge.” Translational Oncology 7 (1): 153–66. [6] Heye, Tobias, et al. "Reproducibility of dynamic contrast-enhanced MR imaging. Part I. Perfusion characteristics in the female pelvis by using multiple computer-aided diagnosis perfusion analysis solutions." Radiology 266.3 (2013): 801-811.

Arterial Input Function (AIF) [7] Fedorov, Andriy, Jacob Fluckiger, Gregory D Ayers, Xia Li, Sandeep N Gupta, Clare Tempany, Robert Mulkern, Thomas E Yankeelov, and Fiona M Fennessy. 2014. “A Comparison of Two Methods for Estimating DCE-MRI Parameters via Individual and Cohort Based AIFs in Prostate Cancer: A Step towards Practical Implementation.” Magnetic Resonance Imaging 32 (4). Elsevier Inc. 321–29. [8] Weinmann HJ, Laniado M, Mützel W. Pharmacokinetics of GdDTPA/dimeglu- mine after intravenous injection into healthy volunteers. Physiol Chem Phys Med NMR January 1984;16(2):167–72. [9] Parker GJM, Roberts C, Macdonald A, Buonaccorsi GA, Cheung S, Buckley DL, et al. Experimentally-derived functional form for a population-averaged high-tem- poral-resolution arterial input function for dynamic contrast-enhanced MRI. Magn Reson Med November 2006;56(5):993–1000. [10] Chen J, Yao J, Thomasson D. Automatic determination of arterial input function for dynamic contrast enhanced MRI in tumor assessment. Med Image Comput Comput Assist Interv January 2008;11(Pt. 1):594–601. [11] Kozlowski P, Chang SD, Meng R, Mädler B, Bell R, Jones EC, et al. Combined prostate diffusion tensor imaging and dynamic contrast enhanced MRI at 3 T– quantitative correlation with biopsy. Magn Reson Imaging June 2010;28(5): 621–8. [12] Ashton E, Raunig D, Ng C, Kelcz F, McShane T, Evelhoch J. Scan-rescan variability in perfusion assessment of tumors in MRI using both model and data-derived arterial input functions. J Magn Reson Imaging September 2008;28(3):791–6. [13] Ahearn TS, Staff RT, Redpath TW, Semple SIK. The effects of renal variation upon measurements of perfusion and leakage volume in breast tumours. Phys Med Biol May 21 2004;49(10):2041–51. [14] Li X, Welch EB, Arlinghaus LR, Chakravarthy AB, Xu L, Farley J, et al. A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer. Phys Med Biol September 7 2011;56(17):5753–69. [15] Shanbhag D, Gupta SN, Rajamani K, Zhu Y, Mullick R. *"A generalized methodology for detection of vascular input function with dynamic contrast enhanced perfusion data. ISMRM’12. Vol. 10; 2012. p. 13004.

Bolus Arrival Time (BAT) [16] Meyer, Ernst. “Simultaneous Correction for Tracer Arrival Delay and Dispersion in CBF Measurements by the H2150 Autoradiographic Method and Dynamic PET.” (1989): n. pag. [17] Calamante, F, D G Gadian, and a Connelly. “Delay and Dispersion Effects in Dynamic Susceptibility Contrast MRI: Simulations Using Singular Value Decomposition.” Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 44.3 (2000): 466–73. [18] Huisman, H J, M R Engelbrecht, and J O Barentsz. 2001. “Accurate Estimation of Pharmacokinetic Contrast-Enhanced Dynamic MRI Parameters of the Prostate.” Journal of Magnetic Resonance Imaging: JMRI 13 (4): 607–14. [19] Cheong, L H, T S Koh, and Z Hou. 2003. “An Automatic Approach for Estimating Bolus Arrival Time in Dynamic Contrast MRI Using Piecewise Continuous Regression Models.” Physics in Medicine and Biology 48 (5): N83–8. [20] Singh, Anup, Ram K Singh Rathore, Mohammad Haris, Sanjay K Verma, Nuzhat Husain, and Rakesh K Gupta. 2009. “Improved Bolus Arrival Time and Arterial Input Function Estimation for Tracer Kinetic Analysis in DCE-MRI.” Journal of Magnetic Resonance Imaging: JMRI 29 (1): 166–76. [21] Orton, Matthew R, David J Collins, Simon Walker-Samuel, James A d’ Arcy, David J Hawkes, David Atkinson, and Martin O Leach. 2007. “Bayesian Estimation of Pharmacokinetic Parameters for DCE-MRI with a Robust Treatment of Enhancement Onset Time.” Physics in Medicine and Biology 52 (9): 2393–2408.

Sampling Time [22] Henderson, E, BK Rutt, and TY Lee. “Temporal Sampling Requirements for the Tracer Kinetics Modeling of Breast Disease.” Magnetic resonance imaging 16.9 (1998): 1057–1073.

Applications in Oncology [23] Padhani, Anwar R. "Dynamic contrast‐enhanced MRI in clinical oncology: Current status and future directions." Journal of Magnetic Resonance Imaging 16.4 (2002): 407-422. [24] Knopp MV, Giesel FL, Marcos H et al. "Dynamic contrast-enhanced magnetic resonance imaging in oncology." Top Magn Reson Imaging, 2001; 12:301-308. [25] 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.