Estimating the discretization dependent accuracy of perfusion in coupled capillary flow measurements

One-compartment models are widely used to quantify hemodynamic parameters such as perfusion, blood volume and mean transit time. These parameters are routinely used for clinical diagnosis and monitoring of disease development and are thus of high relevance. However, it is known that common estimation techniques are discretization dependent and values can be erroneous. In this paper we present a new model that enables systematic quantification of discretization errors. Specifically, we introduce a continuous flow model for tracer propagation within the capillary tissue, used to evaluate state-of-the-art one-compartment models. We demonstrate that one-compartment models are capable of recovering perfusion accurately when applied to only one compartment, i.e. the whole region of interest. However, substantial overestimation of perfusion occurs when applied to fractions of a compartment. We further provide values of the estimated overestimation for various discretization levels, and also show that overestimation can be observed in real-life applications. Common practice of using compartment models for fractions of tissue violates model assumptions and careful interpretation is needed when using the computed values for diagnosis and treatment planning.

[1]  Janet S. Reddin,et al.  Voxel-level comparison of arterial spin-labeled perfusion MRI and FDG-PET in Alzheimer disease , 2011, Neurology.

[2]  S. Patankar Numerical Heat Transfer and Fluid Flow , 2018, Lecture Notes in Mechanical Engineering.

[3]  Franck Plouraboué,et al.  On the Normalization of Cerebral Blood Flow , 2013, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[4]  C B Grandin,et al.  Whole brain quantitative CBF, CBV, and MTT measurements using MRI bolus tracking: Implementation and application to data acquired from hyperacute stroke patients , 2000, Journal of magnetic resonance imaging : JMRI.

[5]  B. Rosen,et al.  High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: Mathematical approach and statistical analysis , 1996, Magnetic resonance in medicine.

[6]  T. Sochi,et al.  A computationally efficient framework for the simulation of cardiac perfusion using a multi‐compartment Darcy porous‐media flow model , 2013, International journal for numerical methods in biomedical engineering.

[7]  D. Cho,et al.  Hemorheology and Microvascular Disorders , 2011, Korean circulation journal.

[8]  Craig T Simmons,et al.  The compleat Darcy: New lessons learned from the first English translation of les fontaines publiques de la Ville de Dijon , 2005, Ground water.

[9]  B. Rosen,et al.  Tracer arrival timing‐insensitive technique for estimating flow in MR perfusion‐weighted imaging using singular value decomposition with a block‐circulant deconvolution matrix , 2003, Magnetic resonance in medicine.

[10]  Jinhua Sun,et al.  Voxel-level comparison of arterial spin-labeled perfusion magnetic resonance imaging in adolescents with internet gaming addiction , 2013, Behavioral and Brain Functions.

[11]  Mary I Townsley,et al.  Structure and composition of pulmonary arteries, capillaries, and veins. , 2012, Comprehensive Physiology.

[12]  Hiroki Shirato,et al.  Differences in CT perfusion maps generated by different commercial software: quantitative analysis by using identical source data of acute stroke patients. , 2010, Radiology.

[13]  T A Gennarelli,et al.  Cerebral blood flow and metabolism in comatose patients with acute head injury. Relationship to intracranial hypertension. , 1984, Journal of neurosurgery.

[14]  E Klotz,et al.  Perfusion measurements of the brain: using dynamic CT for the quantitative assessment of cerebral ischemia in acute stroke. , 1999, European journal of radiology.

[15]  R M Henkelman,et al.  Does IVIM measure classical perfusion? , 1990, Magnetic resonance in medicine.

[16]  S. Heiland,et al.  MR Perfusion-derived Hemodynamic Parametric Response Mapping of Bevacizumab Efficacy in Recurrent Glioblastoma. , 2016, Radiology.

[17]  Susumu Mori,et al.  3D Brain Fiber Reconstruction from Diffusion MRI , 1998, NeuroImage.

[18]  K. Zierler Indicator Dilution Methods for Measuring Blood Flow, Volume, and Other Properties of Biological Systems: A Brief History and Memoir , 2000, Annals of Biomedical Engineering.

[19]  M. Mokin,et al.  Whole-Brain Computed Tomographic Perfusion Imaging in Acute Cerebral Venous Sinus Thrombosis , 2016, Interventional Neurology.

[20]  W. D. Evans,et al.  PARTIAL DIFFERENTIAL EQUATIONS , 1941 .

[21]  Steven P. Sourbron,et al.  A Tracer-Kinetic Field Theory for Medical Imaging , 2014, IEEE Transactions on Medical Imaging.

[22]  B. van Ginneken,et al.  TIPS bilateral noise reduction in 4D CT perfusion scans produces high-quality cerebral blood flow maps , 2011, Physics in medicine and biology.

[23]  Steven A Bogen,et al.  Clinical laboratory measurement of serum, plasma, and blood viscosity. , 2006, American journal of clinical pathology.

[24]  Makoto Sasaki,et al.  Bayesian Hemodynamic Parameter Estimation by Bolus Tracking Perfusion Weighted Imaging , 2012, IEEE Transactions on Medical Imaging.

[25]  Knut-Andreas Lie,et al.  An Introduction to the Numerics of Flow in Porous Media using Matlab , 2007, Geometric Modelling, Numerical Simulation, and Optimization.

[26]  F. Calamante Arterial input function in perfusion MRI: a comprehensive review. , 2013, Progress in nuclear magnetic resonance spectroscopy.

[27]  Steven P Sourbron,et al.  Classic models for dynamic contrast‐enhanced MRI , 2013, NMR in biomedicine.

[28]  R. Chabiniok,et al.  A novel porous mechanical framework for modelling the interaction between coronary perfusion and myocardial mechanics , 2012, Journal of biomechanics.

[29]  Renate Grüner,et al.  Magnetic resonance brain perfusion imaging with voxel‐specific arterial input functions , 2006, Journal of magnetic resonance imaging : JMRI.