Anatomy and physiology
Pharmacokinetic analysis of tissue microcirculation using nested models: Multimodel inference and parameter identifiability
Abstract
The purpose of this study is to evaluate the identifiability of physiological tissue parameters by pharmacokinetic modeling of concentration‐time curves derived under conditions that are realistic for dynamic‐contrast‐enhanced (DCE) imaging and to assess the information‐theoretic approach of multimodel inference using nested models. Tissue curves with a realistic noise level were simulated by means of an axially distributed multipath reference model using typical values reported in literature on plasma flow, permeability‐surface area product, and volume fractions of the intravascular and interstitial space. The simulated curves were subsequently analyzed by a two‐compartment model containing these physiological quantities as fit parameters as well as by two reduced models with only three and two parameters formulated for the case of a permeability‐limited and a flow‐limited scenario, respectively. The competing models were ranked according to Akaike's information criterion (AIC), balancing the bias versus variance trade‐off. To utilize the information available from all three models, model‐averaged parameters were estimated using Akaike weights that quantify the relative strength of evidence in favor of each model. As compared to the full model, the reduced models yielded equivalent or even superior AIC values for scenarios where the structural information in the tissue curves on either the plasma flow or the capillary permeability was limited. Multimodel inference took effect to a considerable extent in half of the curves and improved the precision of the estimated tissue parameters. As theoretically expected, the plasma flow was subject to a systematic (but largely correctable) overestimation, whereas the other three physiological tissue parameters could be determined in a numerically robust and almost unbiased manner. The presented concept of pharmacokinetic analysis of noisy DCE data using three nested models under an information‐theoretic paradigm offers promising prospects for the noninvasive quantification of physiological tissue parameters.
Citing Literature
Number of times cited: 46
- Leonidas Georgiou, Nisha Sharma, David A. Broadbent, Daniel J. Wilson, Barbara J. Dall, Anmol Gangi and David L. Buckley, Estimating breast tumor blood flow during neoadjuvant chemotherapy using interleaved high temporal and high spatial resolution MRI, Magnetic Resonance in Medicine, 79, 1, (317-326), (2017).
- Adam J. Davis, Razia Rehmani, Ashok Srinivasan and Girish M. Fatterpekar, Perfusion and Permeability Imaging for Head and Neck Cancer, Magnetic Resonance Imaging Clinics of North America, 26, 1, (19), (2018).
- Karl P. Kunze, Christoph Rischpler, Carmel Hayes, Tareq Ibrahim, Karl‐Ludwig Laugwitz, Axel Haase, Markus Schwaiger and Stephan G. Nekolla, Measurement of extracellular volume and transit time heterogeneity using contrast‐enhanced myocardial perfusion MRI in patients after acute myocardial infarction, Magnetic Resonance in Medicine, 77, 6, (2320-2330), (2016).
- Gunnar Brix, Mona Salehi Ravesh and Jürgen Griebel, Two‐compartment modeling of tissue microcirculation revisited, Medical Physics, 44, 5, (1809-1822), (2017).
- Siamak P. Nejad‐Davarani, Hassan Bagher‐Ebadian, James R. Ewing, Douglas C. Noll, Tom Mikkelsen, Michael Chopp and Quan Jiang, A parametric model of the brain vascular system for estimation of the arterial input function (AIF) at the tissue level, NMR in Biomedicine, 30, 5, (2017).
- Blandine Romain, Laurence Rouet, Daniel Ohayon, Olivier Lucidarme, Florence d’Alché-Buc and Véronique Letort, Parameter estimation of perfusion models in dynamic contrast-enhanced imaging: a unified framework for model comparison, Medical Image Analysis, 10.1016/j.media.2016.07.008, 35, (360-374), (2017).
- Anna K. Heye, Michael J. Thrippleton, Paul A. Armitage, Maria del C. Valdés Hernández, Stephen D. Makin, Andreas Glatz, Eleni Sakka and Joanna M. Wardlaw, Tracer kinetic modelling for DCE-MRI quantification of subtle blood–brain barrier permeability, NeuroImage, 10.1016/j.neuroimage.2015.10.018, 125, (446-455), (2016).
- Tammo Rukat, Simon Walker‐Samuel and Stefan A. Reinsberg, Dynamic contrast‐enhanced MRI in mice: An investigation of model parameter uncertainties, Magnetic Resonance in Medicine, 73, 5, (1979-1987), (2014).
- Gunnar Brix, Ursula Lechel, Elke Nekolla, Jürgen Griebel and Christoph Becker, Radiation protection issues in dynamic contrast-enhanced (perfusion) computed tomography, European Journal of Radiology, 84, 12, (2347), (2015).
- Sang Ho Lee, Koichi Hayano, Andrew X. Zhu, Dushyant V. Sahani and Hiroyuki Yoshida, Advanced Hepatocellular Carcinoma, Journal of Computer Assisted Tomography, 10.1097/RCT.0000000000000288, 39, 5, (687-696), (2015).
- Julia C. Sommer and Volker J. Schmid, Spatial two‐tissue compartment model for dynamic contrast‐enhanced magnetic resonance imaging, Journal of the Royal Statistical Society: Series C (Applied Statistics), 63, 5, (695-713), (2014).
- Julia C. Sommer, Jan Gertheiss and Volker J. Schmid, Spatially regularized estimation for the analysis of dynamic contrast‐enhanced magnetic resonance imaging data, Statistics in Medicine, 33, 6, (1029-1041), (2013).
- Fahmi Khalifa, Ahmed Soliman, Ayman El‐Baz, Mohamed Abou El‐Ghar, Tarek El‐Diasty, Georgy Gimel'farb, Rosemary Ouseph and Amy C. Dwyer, Models and methods for analyzing DCE‐MRI: A review, Medical Physics, 41, 12, (2014).
- Jesper Folsted Kallehauge, Kari Tanderup, Chong Duan, Søren Haack, Erik Morre Pedersen, Jacob Christian Lindegaard, Lars Ulrik Fokdal, Sandy Mohamed Ismail Mohamed and Thomas Nielsen, Tracer kinetic model selection for dynamic contrast-enhanced magnetic resonance imaging of locally advanced cervical cancer, Acta Oncologica, 53, 8, (1064), (2014).
- Stephanie L. Barnes, Jennifer G. Whisenant, Mary E. Loveless, Gregory D. Ayers and Thomas E. Yankeelov, Assessing the reproducibility of dynamic contrast enhanced magnetic resonance imaging in a murine model of breast cancer, Magnetic Resonance in Medicine, 69, 6, (1721-1734), (2012).
- Prativa Sahoo, Ram K.S. Rathore, Rishi Awasthi, Bhaswati Roy, Sanjay Verma, Divya Rathore, Sanjay Behari, Mazhar Husain, Nuzhat Husain, Chandra M. Pandey, Sudipta Mohakud and Rakesh K. Gupta, Subcompartmentalization of extracellular extravascular space (EES) into permeability and leaky space with local arterial input function (AIF) results in improved discrimination between high‐ and low‐grade glioma using dynamic contrast‐enhanced (DCE) MRI, Journal of Magnetic Resonance Imaging, 38, 3, (677-688), (2013).
- Anders Garpebring, Patrik Brynolfsson, Jun Yu, Ronnie Wirestam, Adam Johansson, Thomas Asklund and Mikael Karlsson, Uncertainty estimation in dynamic contrast‐enhanced MRI, Magnetic Resonance in Medicine, 69, 4, (992-1002), (2012).
- James R. Ewing and Hassan Bagher‐Ebadian, Model selection in measures of vascular parameters using dynamic contrast‐enhanced MRI: experimental and clinical applications, NMR in Biomedicine, 26, 8, (1028-1041), (2013).
- Steven P. Sourbron and David L. Buckley, Classic models for dynamic contrast‐enhanced MRI, NMR in Biomedicine, 26, 8, (1004-1027), (2013).
- C.A. Cuenod and D. Balvay, Perfusion and vascular permeability: Basic concepts and measurement in DCE-CT and DCE-MRI, Diagnostic and Interventional Imaging, 10.1016/j.diii.2013.10.010, 94, 12, (1187-1204), (2013).
- C.-A. Cuenod and D. Balvay, Imagerie de la perfusion tissulaire et de la perméabilité, Journal de Radiologie Diagnostique et Interventionnelle, 10.1016/j.jradio.2013.08.011, 94, 12, (1184-1202), (2013).
- Michael Ingrisch and Steven Sourbron, Tracer-kinetic modeling of dynamic contrast-enhanced MRI and CT: a primer, Journal of Pharmacokinetics and Pharmacodynamics, 10.1007/s10928-013-9315-3, 40, 3, (281-300), (2013).
- Hassan Bagher‐Ebadian, Rajan Jain, Siamak P. Nejad‐Davarani, Tom Mikkelsen, Mei Lu, Quan Jiang, Lisa Scarpace, Ali S. Arbab, Jayant Narang, Hamid Soltanian‐Zadeh, Ramesh Paudyal and James. R. Ewing, Model selection for DCE‐T1 studies in glioblastoma, Magnetic Resonance in Medicine, 68, 1, (241-251), (2011).
- Gunnar Brix, Mona Salehi Ravesh, Stefan Zwick, Jürgen Griebel and Stefan Delorme, On impulse response functions computed from dynamic contrast-enhanced image data by algebraic deconvolution and compartmental modeling, Physica Medica, 28, 2, (119), (2012).
- Francisco Garcia-Sevilla, Manuela Garcia-Moreno, Milagros Molina-Alarcon, María J. Garcia-Meseguer, José M. Villalba, Enrique Arribas and Ramón Varon, Linear compartmental systems. I. kinetic analysis and derivation of their optimized symbolic equations, Journal of Mathematical Chemistry, 50, 6, (1598), (2012).
- Steven Sourbron, Wieland H. Sommer, Maximilian F. Reiser and Christoph J. Zech, Combined Quantification of Liver Perfusion and Function with Dynamic Gadoxetic Acid–enhanced MR Imaging, Radiology, 10.1148/radiol.12110337, 263, 3, (874-883), (2012).
- M. O. Leach, B. Morgan, P. S. Tofts, D. L. Buckley, W. Huang, M. A. Horsfield, T. L. Chenevert, D. J. Collins, A. Jackson, D. Lomas, B. Whitcher, L. Clarke, R. Plummer, I. Judson, R. Jones, R. Alonzi, T. Brunner, D. M. Koh, P. Murphy, J. C. Waterton, G. Parker, M. J. Graves, T. W. J. Scheenen, T. W. Redpath, M. Orton, G. Karczmar, H. Huisman, J. Barentsz and A. Padhani, Imaging vascular function for early stage clinical trials using dynamic contrast-enhanced magnetic resonance imaging, European Radiology, 10.1007/s00330-012-2446-x, 22, 7, (1451-1464), (2012).
- S P Sourbron and D L Buckley, Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability, Physics in Medicine and Biology, 10.1088/0031-9155/57/2/R1, 57, 2, (R1-R33), (2011).
- Robert Luypaert, Michael Ingrisch, Steven Sourbron and Johan de Mey, The Akaike information criterion in DCE-MRI: Does it improve the haemodynamic parameter estimates?, Physics in Medicine and Biology, 57, 11, (3609), (2012).
- Johannes G. Korporaal, Marco van Vulpen, Cornelis A. T. van den Berg and Uulke A. van der Heide, Tracer Kinetic Model Selection for Dynamic Contrast-Enhanced Computed Tomography Imaging of Prostate Cancer, Investigative Radiology, 10.1097/RLI.0b013e31821c0ea7, 47, 1, (41-48), (2012).
- G. Brix, J. Griebel and S. Delorme, Dynamische kontrastverstärkte Computertomographie, Der Radiologe, 52, 3, (277), (2012).
- Michael Ingrisch, Steven Sourbron, Dominik Morhard, Birgit Ertl-Wagner, Tania Kümpfel, Reinhard Hohlfeld, Maximilian Reiser and Christian Glaser, Quantification of Perfusion and Permeability in Multiple Sclerosis, Investigative Radiology, 10.1097/RLI.0b013e31823bfc97, 47, 4, (252-258), (2012).
- Tong San Koh, Sotirios Bisdas, Dow Mu Koh and Choon Hua Thng, Fundamentals of tracer kinetics for dynamic contrast‐enhanced MRI, Journal of Magnetic Resonance Imaging, 34, 6, (1262-1276), (2011).
- Steven P. Sourbron and David L. Buckley, On the scope and interpretation of the Tofts models for DCE‐MRI, Magnetic Resonance in Medicine, 66, 3, (735-745), (2011).
- J. M. Villalba, A. J. Barbero, R. Diaz‐Sierra, E. Arribas, M. J. Garcia‐Meseguer, F. Garcia‐Sevilla, M. Garcia‐Moreno, J. A. Vidal De Labra and R. Varon, Computerized evaluation of mean residence times in multicompartmental linear system and pharmacokinetics, Journal of Computational Chemistry, 32, 5, (915-931), (2010).
- Janine Ring, Thorsten Persigehl, Stefanie Remmele, Walter Heindel, Hannes Dahnke and Christoph Bremer, Monitoring of Bevacizumab-Induced Antiangiogenic Treatment Effects By “Steady State” Ultrasmall Superparamagnetic Iron Oxide Particles Magnetic Resonance Imaging Using Robust Multiecho ΔR2* Relaxometry, Investigative Radiology, (1), (2011).
- Gunnar Brix, Ursula Lechel, Markus Petersheim, Radko Krissak and Christian Fink, Dynamic Contrast-Enhanced CT Studies, Investigative Radiology, 46, 1, (64), (2011).
- R Luypaert, S Sourbron, S Makkat and J de Mey, Error estimation for perfusion parameters obtained using the two-compartment exchange model in dynamic contrast-enhanced MRI: a simulation study, Physics in Medicine and Biology, 10.1088/0031-9155/55/21/006, 55, 21, (6431-6443), (2010).
- Gunnar Brix, Jürgen Griebel, Fabian Kiessling and Frederik Wenz, Tracer kinetic modelling of tumour angiogenesis based on dynamic contrast-enhanced CT and MRI measurements, European Journal of Nuclear Medicine and Molecular Imaging, 37, S1, (30), (2010).
- Gunnar Brix, Stefan Zwick, Jürgen Griebel, Christian Fink and Fabian Kiessling, Estimation of tissue perfusion by dynamic contrast-enhanced imaging: simulation-based evaluation of the steepest slope method, European Radiology, 20, 9, (2166), (2010).
- Steven Sourbron, Technical aspects of MR perfusion, European Journal of Radiology, 10.1016/j.ejrad.2010.02.017, 76, 3, (304-313), (2010).
- Stefan Zwick, Gunnar Brix, Paul S. Tofts, Ralph Strecker, Annette Kopp-Schneider, Hendrik Laue, Wolfhard Semmler and Fabian Kiessling, Simulation-based comparison of two approaches frequently used for dynamic contrast-enhanced MRI, European Radiology, 10.1007/s00330-009-1556-6, 20, 2, (432-442), (2009).
- M. Feilke, B. Bischl, V. J. Schmid and J. Gertheiss, Boosting in Nonlinear Regression Models with an Application to DCE-MRI Data, Methods of Information in Medicine, 10.3414/ME14-01-0131, 55, 1, (31-41), (2015).
- C Debus, R Floca, D Nörenberg, A Abdollahi and M Ingrisch, Impact of fitting algorithms on errors of parameter estimates in dynamic contrast-enhanced MRI, Physics in Medicine & Biology, 10.1088/1361-6560/aa8989, 62, 24, (9322-9340), (2017).
- T S Koh, T P Hennedige, C H Thng, S Hartono and Q S Ng, Understanding K trans : a simulation study based on a multiple-pathway model , Physics in Medicine & Biology, 10.1088/1361-6560/aa70c9, 62, 13, (N297-N319), (2017).
- David L. Buckley, Shutter‐speed dynamic contrast‐enhanced MRI: Is it fit for purpose?, Magnetic Resonance in Medicine, , (2018).
