A model for mass diffusion in infarcted tissues by using LBM

다국어 초록 (Multilingual Abstract)

In the present study, a new mathematical model for mass transfer in extra-vascular space is presented to indicate the effect of dead cells in infarcted tissues. This model is useful for predicting the results of experiments which are designed to distinguish normal from infarcted tissues and it can be used to approximate the percent of damaged cells in the infarcted tissue. The increase of permeability due to existence of dead cells caused by infarction is modeled as a sink term. The present model is validated for two different solutes by comparing their results with the existing experimental data. It is shown that the presented model is an appropriate model for simulating mass transfer in tissues. Furthermore, the sensitivity of the model to the effective diffusion coefficient, Schmidt number, porosity and Damkohler number is examined. The simulations show that permeability of dead cells significantly affects the volume of the contrast agent in the infarcted tissue. The results also indicate that the Schmidt number not only affects the mass diffusion through damaged tissues, but also influences the partition coefficient. The other parameters mentioned above have effects on the amount of mass diffusion into the infarcted tissue when the extent of damage in tissues is changed.

참고문헌 (Reference)

1 R. S. Pereira, "The use of Gd-DTPA as a marker of myocardial viability in reperfused acute myocardial infarction" 17 : 395-404, 2001

2 A. C. Guyton, "Textbook of Medical Physiology" Elsevier Saunders 2006

3 K. Hirata, "Reginal partition coefficient of water in patients with cerebrovascular disease and its effect on rCBF assessment" 32 : 63-70, 2011

4 K. Vafai, "Porous Media Application in Biological Systems and Biotechnology" Taylor & Francis Group 2011

5 G. R. Moran, "Myocardial viability imaging using Gd-DTPA: Physiological modeling of infracted myocardium and impact on injection strategy and imaging time" 48 : 791-800, 2002

6 L. Saba, "Multimodality Atherosclerosis Imaging and Diagnosis" Springer Science & Business Media 2013

7 N. Yang, "Modeling of low-density lipoprotein(LDL)transport in the artery effects of hypertension" 49 : 850-867, 2006

8 Mahmoud Jourabian, "Melting of nanoparticles-enhanced phase change material (NEPCM) in vertical semicircle enclosure: numerical study" 대한기계학회 29 (29): 3819-3830, 2015

9 Y. M. Xuan, "Investigation on mass diffusion process in porous media based on lattice Boltzmann method" 46 : 1039-1051, 2010

10 D. D’Humiμeres, "Generalized lattice Boltzmann equations in rarefied gas dynamics: theory and simulations" 159 : 450-458, 1992

11 E. Yahaghi, "Estimation of contrast agent concentration in intra-and extra-vascular spaces of brain tissue" 204 : 102-118, 2006

12 N. Kawel, "Effect of protein binding substances on T1times and the partition coefficient in contrast-enhanced cardiac magnetic resonance imaging" 15 : 1-2, 2013

13 R. L. Fournier, "Basic transport phenomena in biomedical engineering" Taylor & Francis 1998

14 X. He, "Analysis and boundary condition of the lattice Boltzmann BGK model with two velocity components" 87 : 115-136, 1995

15 A. N. Cookson, "A spatially-distributed computational model to quantify behavior of contrast agents in MR perfusion imaging" 18 : 1200-1216, 2014

16 J. L. Gevertz, "A novel three-phase model of brain tissue microstructure" 5 : 1-9, 2008

17 Q. Liu, "A multiplerelaxation-time lattice Boltzmann model for convection heat transfer in porous media" 73 : 761-775, 2014

18 J. A. Johnson, "A model for capillary exchange" 210 : 1299-1303, 1966

19 Z. H. Guo, "A lattice Boltzmann model for convection heat transfer in porous media" 48 : 157-177, 2005

20 J. Wang, "A lattice Boltzmann algorithm for fluid-solid conjugate heat transfer" 46 : 228-234, 2007