A computational model for microcirculation including Fahraeus‐Lindqvist effect, plasma skimming and fluid exchange with the tissue interstitium

We present a two‐phase model for microcirculation that describes the interaction of plasma with red blood cells. The model takes into account of typical effects characterizing the microcirculation, such as the Fahraeus‐Lindqvist effect and plasma skimming. Besides these features, the model describes the interaction of capillaries with the surrounding tissue. More precisely, the model accounts for the interaction of capillary transmural flow with the surrounding interstitial pressure. Furthermore, the capillaries are represented as one‐dimensional channels with arbitrary, possibly curved configuration. The latter two features rely on the unique ability of the model to account for variations of flow rate and pressure along the axis of the capillary, according to a local differential formulation of mass and momentum conservation. Indeed, the model stands on a solid mathematical foundation, which is also addressed in this work. In particular, we present the model derivation, the variational formulation, and its approximation using the finite element method. Finally, we conclude the work with a comparative computational study of the importance of the Fahraeus‐Lindqvist, plasma skimming, and capillary leakage effects on the distribution of flow in a microvascular network.

[1]  Anna Scotti,et al.  A mixed finite element method for modeling the fluid exchange between microcirculation and tissue interstitium , 2018 .

[2]  Barbara Wohlmuth,et al.  Mathematical modelling, analysis and numerical approximation of second order elliptic problems with inclusions , 2018 .

[3]  Erika Kristina Lindstrøm,et al.  Comparison of phase-contrast MR and flow simulations for the study of CSF dynamics in the cervical spine , 2018, The neuroradiology journal.

[4]  Erika Kristina Lindstrøm,et al.  Non-invasive assessment of pulsatile intracranial pressure with phase-contrast magnetic resonance imaging , 2017, PloS one.

[5]  S. A. Khonsary Guyton and Hall: Textbook of Medical Physiology , 2017, Surgical Neurology International.

[6]  Rebecca J Shipley,et al.  Insights into cerebral haemodynamics and oxygenation utilising in vivo mural cell imaging and mathematical modelling , 2017, Scientific Reports.

[7]  E. Kuhl,et al.  A family of hyperelastic models for human brain tissue , 2017 .

[8]  Anders M. Dale,et al.  Interstitial solute transport in 3D reconstructed neuropil occurs by diffusion rather than bulk flow , 2017, Proceedings of the National Academy of Sciences.

[9]  J. Marcickiewicz,et al.  Late-week surgical treatment of endometrial cancer is associated with worse long-term outcome: Results from a prospective, multicenter study , 2017, PloS one.

[10]  Patrick Jenny,et al.  The relative influence of hematocrit and red blood cell velocity on oxygen transport from capillaries to tissue , 2017, Microcirculation.

[11]  Timothy W. Secomb,et al.  Blood Flow in the Microcirculation , 2017 .

[12]  Timothy W Secomb,et al.  A Green's function method for simulation of time-dependent solute transport and reaction in realistic microvascular geometries. , 2016, Mathematical medicine and biology : a journal of the IMA.

[13]  A. Goriely,et al.  Bulging Brains , 2016, Journal Of Elasticity.

[14]  Paolo Zunino,et al.  A computational study of cancer hyperthermia based on vascular magnetic nanoconstructs , 2016, Royal Society Open Science.

[15]  Anna Devor,et al.  Modeling of Cerebral Oxygen Transport Based on In vivo Microscopic Imaging of Microvascular Network Structure, Blood Flow, and Oxygenation , 2016, Front. Comput. Neurosci..

[16]  Kent-André Mardal,et al.  Preconditioners for Saddle Point Systems with Trace Constraints Coupling 2D and 1D Domains , 2016, SIAM J. Sci. Comput..

[17]  Jack Lee,et al.  Multiphysics and multiscale modelling, data–model fusion and integration of organ physiology in the clinic: ventricular cardiac mechanics , 2016, Interface Focus.

[18]  P Zunino,et al.  Modelling mass and heat transfer in nano-based cancer hyperthermia , 2015, Royal Society Open Science.

[19]  John E. Hall,et al.  Guyton and Hall Textbook of Medical Physiology , 2015 .

[20]  A. Linninger,et al.  Hematocrit Distribution and Tissue Oxygenation in Large Microcirculatory Networks , 2015, Microcirculation.

[21]  P. Zunino,et al.  A computational model of drug delivery through microcirculation to compare different tumor treatments , 2014, International journal for numerical methods in biomedical engineering.

[22]  Barbara I. Wohlmuth,et al.  Optimal A Priori Error Estimates for an Elliptic Problem with Dirac Right-Hand Side , 2014, SIAM J. Numer. Anal..

[23]  Laura Cattaneo,et al.  Computational models for fluid exchange between microcirculation and tissue interstitium , 2014, Networks Heterog. Media.

[24]  Sandip Mitra,et al.  Subcutaneous interstitial pressure and volume characteristics in renal impairment associated with edema. , 2013, Kidney international.

[25]  Jake Olivier,et al.  Bicycle Helmet Wearing Is Not Associated with Close Motor Vehicle Passing: A Re-Analysis of Walker, 2007 , 2013, PloS one.

[26]  Tim David,et al.  A Computational Model of Oxygen Transport in the Cerebrocapillary Levels for Normal and Pathologic Brain Function , 2013, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[27]  Heiko Rieger,et al.  Interstitial Fluid Flow and Drug Delivery in Vascularized Tumors: A Computational Model , 2013, PloS one.

[28]  A. Linninger,et al.  Cerebral Microcirculation and Oxygen Tension in the Human Secondary Cortex , 2013, Annals of Biomedical Engineering.

[29]  G. E. Vates,et al.  A Paravascular Pathway Facilitates CSF Flow Through the Brain Parenchyma and the Clearance of Interstitial Solutes, Including Amyloid β , 2012, Science Translational Medicine.

[30]  Nicolas P Smith,et al.  Estimation of Blood Flow Rates in Large Microvascular Networks , 2012, Microcirculation.

[31]  Graham M Fraser,et al.  Microvascular Flow Modeling using In Vivo Hemodynamic Measurements in Reconstructed 3D Capillary Networks , 2012, Microcirculation.

[32]  Michel Fortin,et al.  Mixed and Hybrid Finite Element Methods , 2011, Springer Series in Computational Mathematics.

[33]  Gerhard Gompper,et al.  Predicting human blood viscosity in silico , 2011, Proceedings of the National Academy of Sciences.

[34]  Mathieu Sellier,et al.  A computational model of hemodynamic parameters in cortical capillary networks. , 2011, Journal of theoretical biology.

[35]  Brett J Tully,et al.  Cerebral water transport using multiple-network poroelastic theory: application to normal pressure hydrocephalus , 2010, Journal of Fluid Mechanics.

[36]  G. Karniadakis,et al.  Blood Flow and Cell‐Free Layer in Microvessels , 2010, Microcirculation.

[37]  Daniel Ruiz,et al.  CFD Parallel Simulation Using Getfem++ and Mumps , 2010, Euro-Par.

[38]  Patrick Jenny,et al.  Red blood cell distribution in simplified capillary networks , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[39]  Sylvie Lorthois,et al.  Branching patterns for arterioles and venules of the human cerebral cortex , 2010, Brain Research.

[40]  B. Weber,et al.  Vascular Graph Model to Simulate the Cerebral Blood Flow in Realistic Vascular Networks , 2009, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[41]  C. Pozrikidis,et al.  Numerical Simulation of Blood Flow Through Microvascular Capillary Networks , 2009, Bulletin of mathematical biology.

[42]  David T. Eddington,et al.  Statistical Dynamics of Flowing Red Blood Cells by Morphological Image Processing , 2009, PLoS Comput. Biol..

[43]  Berend E. Westerhof,et al.  The arterial Windkessel , 2009, Medical & Biological Engineering & Computing.

[44]  Jack Lee,et al.  Theoretical Modeling in Hemodynamics of Microcirculation , 2008, Microcirculation.

[45]  Daniel A Beard,et al.  The Role of Theoretical Modeling in Microcirculation Research , 2008, Microcirculation.

[46]  A. Quarteroni,et al.  Numerical Approximation of Partial Differential Equations , 2008 .

[47]  A. Quarteroni,et al.  On the coupling of 1D and 3D diffusion-reaction equations. Applications to tissue perfusion problems , 2008 .

[48]  E. M. Renkin,et al.  Exchange of Substances Through Capillary Walls , 2008 .

[49]  M. Swartz,et al.  Interstitial flow and its effects in soft tissues. , 2007, Annual review of biomedical engineering.

[50]  A. Pries,et al.  Microvascular blood viscosity in vivo and the endothelial surface layer. , 2005, American journal of physiology. Heart and circulatory physiology.

[51]  T. Secomb,et al.  Estimation of capillary density in human skeletal muscle based on maximal oxygen consumption rates. , 2003, American journal of physiology. Heart and circulatory physiology.

[52]  A. Popel,et al.  A computational study of the effect of capillary network anastomoses and tortuosity on oxygen transport. , 2000, Journal of theoretical biology.

[53]  R. Jain,et al.  Fractal Characteristics of Tumor Vascular Architecture During Tumor Growth and Regression , 1997, Microcirculation.

[54]  R. Carr,et al.  Plasma Skimming in Vascular Trees: Numerical Estimates of Symmetry Recovery Lengths , 1995, Microcirculation.

[55]  A. Pries,et al.  Resistance to blood flow in microvessels in vivo. , 1994, Circulation research.

[56]  Levick Capillary filtration‐absorption balance reconsidered in light of dynamic extravascular factors , 1991, Experimental physiology.

[57]  R K Jain,et al.  Transport of fluid and macromolecules in tumors. II. Role of heterogeneous perfusion and lymphatics. , 1990, Microvascular research.

[58]  T. Secomb,et al.  A Green's function method for analysis of oxygen delivery to tissue by microvascular networks. , 1989, Mathematical biosciences.

[59]  Timothy W. Secomb,et al.  The interaction of extravascular pressure fields and fluid exchange in capillary networks , 1986 .

[60]  Timothy W. Secomb,et al.  Effect of extravascular pressure gradients on capillary fluid exchange , 1986 .

[61]  M Intaglietta,et al.  Capillary flow velocity measurements in vivo and in situ by television methods. , 1975, Microvascular research.

[62]  A. Guyton,et al.  Interstitial Fluid Pressure , 1966, Circulation research.

[63]  R. Mann,et al.  Human Physiology , 1839, Nature.

[64]  Brigitte Maier,et al.  Mixed And Hybrid Finite Element Methods Springer Series In Computational Mathematics , 2016 .

[65]  Ana I. Pereira,et al.  Tracking Red Blood Cells in Microchannels: A Comparative Study Between an Automatic and a Manual Method , 2013 .

[66]  Carlo D'Angelo,et al.  Finite Element Approximation of Elliptic Problems with Dirac Measure Terms in Weighted Spaces: Applications to One- and Three-dimensional Coupled Problems , 2012, SIAM J. Numer. Anal..

[67]  C. Dangelo,et al.  Multiscale modelling of metabolism and transport phenomena in living tissues , 2007 .

[68]  L. T. Baxter,et al.  Transport of fluid and macromolecules in tumors. III. Role of binding and metabolism. , 1991, Microvascular research.

[69]  E. Ortiz Numerical Approximation of Partial Differential Equations. , 1988 .

[70]  Jean E. Roberts,et al.  Mixed and hybrid finite element methods , 1987 .