All ETDs from UAB

Advisory Committee Chair

Roy P Koomullil

Advisory Committee Members

Ahmed Kamel Abdel Aal

Mark Bolding

Louis B Nabors

Arie Nakhmani

Document Type

Dissertation

Date of Award

2020

Degree Name by School

Doctor of Philosophy (PhD) School of Engineering

Abstract

ABSTRACT In 2020, the National Brain Tumor Society reported that physicians would diagnose more than 87,000 new cases of brain tumors. These cases will add to the approximately 700,000 individuals in the United States living with brain and central nervous system tumors. Almost 18,000 of these cases will prove fatal [1]. The blood-brain barrier (BBB) hinders the effective treatment of brain tumors. The task of this barrier is to protect the brain from composition instabilities in blood plasma and shield the brain from agents that may adversely impact brain functionality. However, by performing its tasks the barrier prevents potential effective therapy from penetrating the brain. To overcome the BBB, clinicians currently use mechanisms such as convection-enhanced delivery (CED) and Ommaya devices [2]. These mechanisms use catheters to infuse therapy into the tumors. However, the infusions may not deliver sufficient amounts of therapy, which could fail to destroy the tumors. Failure to destroy the tumors may result from an inadequate selection of the catheter placement, which influences the travel path of the therapy. Therapy travel throughout the brain is directionally dependent and unpredictable because of its anisotropic characteristics. Currently, researchers use diffusion tensor imaging (DTI) to determine the travel path in the brain by measuring the motion of water molecules [3]. The proposed study is the development of a computational methodology to predict the optimal catheter placement for maximum therapy distribution within the tumor. This methodology uses computational fluid dynamics (CFD) to simulate CED to brain tumors and to assess the resulting distribution based on catheter placement. This dissertation research uses T1-weighted imaging (T1) to derive surface models for the geometry and diffusion-weighted imaging (DWI) to derive diffusion tensors for diffusivity. Diffusion tensors quantitatively describe the likely therapy travel direction at a given location and influence the permeability of the therapy. This study includes porosity, interstitial fluid pressure, and the application of a constant rate which influence the therapy distribution. From a selection of catheter placements and using response surface optimization, this study will support the prediction of the optimal placement based on the maximum volume distribution within the tumor. Keywords: convection-enhanced delivery, computational fluid dynamics, tumor

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