Study of Perfusion Kinetics in Human Brain Tumor Using Leaky Tracer Kinetic Model of DCE-MRI Data and CFD

Abstract

A computational fluid dynamics (CFD) model based on realistic voxelized representation of human brain tumor vasculature is presented. The model utilizes dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data to account for heterogeneous porosity and permeability of contrast agent inside the tumor. Patient specific arterial input function (AIF) is employed in this study. Owing to higher accuracy of Leaky Tracer Kinetic Model (LTKM) in shorter duration human imaging data, the model is employed to determine perfusion parameters and compared with General Tracer Kinetic Model (GTKM). The developed CFD model is used to simulate and predict transport, distribution and retention of contrast agent in different parts of human tissue at different times. In future, a patient specific model can be developed to forecast the deposition of drugs and nanoparticles and tune the parameters for thermal ablation of tumors.