Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Doctor of Philosophy

Program

Biomedical Engineering

Supervisor

Peters Terry

2nd Supervisor

Khan Ali

Co-Supervisor

Abstract

Epilepsy constitutes one of the most common neurological clinicopathological entities affecting approximately 1% of the general population. Temporal lobe epilepsy (TLE) represents by far the most common form of medically intractable focal epilepsy in adults. Surgical resection is the common form of treatment when lesions are clearly delineated, either from patient’s magnetic resonance imaging (MRI) structural scans or by invasive seizure monitoring techniques (e.g., intracranial EEG) for patients with non-lesional MRI scans. Increasing numbers of studies have suggested that TLE is more of a network disorder, therefore full delineation of pathological tissue is difficult resulting in incomplete resection, possibly contributing to long-term recurrence of seizures after surgery. Diffusion MRI, an advanced MRI technique that is sensitive to the tissue at the microstructural level, has been studied, hoping to detect subtle microstructural changes related to TLE.

In this thesis, we investigated the ability of a diffusion MRI model, called diffusion kurtosis imaging, (DKI) to quantify TLE patients brain microstructure. Each chapter discusses the method developed to accomplish this, beginning with Chapter 1 giving the general background and the motivation behind this thesis. Chapter 2 develops a method of assessing the reproducibility in whole-brain high-resolution DKI at varying b-values. A shorter protocol was identified with comparable precision as the protocol with three b-values, supporting DKI for aiding clinical tools to assess brain tissue microstructure. Chapter 3 focuses on identifying microstructural abnormalities in the white matter (WM) and grey matter (GM) of the temporal pole, a region underappreciated in TLE patients. The method developed combining DKI measurements and tract-specific analysis uncovered temporal pole microstructural abnormalities in TLE patients (includes non-lesional TLE patients) compared to healthy controls. The work described in Chapter 4 explores a machine learning approach to laterialize TLE patients, demonstrating that DKI-based classifiers obtained slight increase in their general accuracy for GM region. Finally, Chapter 5 discusses the contributions of the thesis and provide suggestions for future research.

Summary for Lay Audience

Temporal lobe epilepsy is a medical condition that affects the temporal lobe region of the brain, and is commonly treated with anti-epileptic drugs. However, for some of the TLE patients who do not respond to medication, surgery is the preferred method of treatment. Before surgery is performed, the seizure focus must be identified. Magnetic resonance imaging is a technique that has been used to image TLE patients, but sometimes the scans of these patients are reported as being normal (i.e., no sign of abnormality). Therefore, more invasive monitoring techniques must be employed to isolate the seizure focus. Following surgery, long-term follow up has indicated that seizures recur in some of these patients. Several studies have attributed this to in-complete resection of abnormal brain areas, which may not be confined solely to the temporal lobe.

In this thesis, we investigated an advanced MRI technique, commonly known as diffusion MRI, that detects the movement of water in the brain, to provide indirect information relating to the underlying microstructure. Using the signal measured with diffusion MRI, many models have been developed to quantify the microstructure property. One such model is called diffusion kurtosis imaging (DKI), which aims to quantify the complex tissue microstructure.

Chapter 1 provides more detailed background and motivation behind this thesis; Chapter 2 discusses our approach to assess the precision of the DKI model. It was concluded that high precision can still be achieved within reasonable scan time, supporting the use of DKI for clinical application. Chapter 3 describes the method developed to detect microstructural changes within the temporal pole region of the temporal lobe in TLE patients. Basically, we extracted DKI measurements along two WM fiber bundles connected to the temporal pole and the deep grey matter. Our findings demonstrated that by combining DKI and other analysis techniques, diffusion abnormalities related to TLE can be uncovered within the temporal pole. Chapter 4 focuses on a machine learning approach for classifying a cohort of TLE patients into left or right TLE according to the side on which the lesions are detected. The overall accuracy measurements demonstrated that DKI based classifiers have slightly better performance in the GM region compared to DTI classifiers. Finally, the thesis ends with Chapter 5, which provides general conclusions and plans for future work.

Download

Share

COinS