Development of brain tissue swelling predictive tools for ischaemic stroke patient post-treatment

• Main Authors: Mohamed Mokhtarudin, Mohd Jamil, Oumer, Ahmed Nurye, Adib, M. A.H.M., Jalıl, Muhammad Hilmi, Einly, Lim, Ahmad Hafiz, Zulkifly
• Format: Research Report
• Language: English
• Published: 2019
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://umpir.ump.edu.my/id/eprint/36321/1/Development%20of%20brain%20tissue%20swelling%20predictive%20tools%20for%20ischaemic%20stroke%20patient%20post-treatment.pdf; http://umpir.ump.edu.my/id/eprint/36321/

Ischaemic stroke is one of the causes of death worldwide. Treatments such as thrombolysis and catheterisation must be given within 3 hours after stroke onset, in which treatments beyond this time may pose risk of brain tissue swelling. Thus, a prediction system must be made to determine the suitability of a stroke treatment to avoid the risk of failure. In this report, a mathematical model based on poroelastic theory and asymptotic expansion homogenization has been developed to study the formation of brain tissue swelling after ischaemia-reperfusion treatment. Firstly, the mathematical model of brain tissue swelling after ischaemia-reperfusion treatment is investigated using an ideal 2D brain geometry. The objective here is to observe the effect of infarct size and location towards the formation and severity of brain herniation, which will form due to brain tissue swelling. However, this model assumed that the blood pressure is constant and homogeneous throughout the brain, while in fact, the blood capillaries vary in sizes and shapes. Therefore, asymptotic expansion homogenization technique is applied to allow for the inclusion of capillaries sizes into the initial model. This method transforms the initial model into two types of equations: (1) macroscale governing equations; and (2) microscale cell problems. In order to solve for the macroscale governing equations, the microscale cell problems must first be solved on a brain tissue geometry to calculate the effective parametric tensors, which later be used in the macroscale governing equations. Lastly, the mathematical model is solved in a realistic brain geometry to evcaluate the effect of different mechanical properties of the brain towards brain tissue swelling formation.