Computational in vivo tissue characterisation for multi-contrast high-resolution magnetic resonance imaging data

Abstract

The human brain is a remarkably complex organ. Comprehension of the tissue properties of the brain and the multitude of changes in disease and ageing may lead to a greater appreciation and understanding of human biology. Combined, Magnetic Resonance Imaging (MRI) and image processing techniques are now capable of resolving very high-resolution images of the brain, including characterising discrete tissue types within small brain structures such as the hippocampus. The hippocampus is a small structure most commonly associated with its function in memory, and is comprised of subfields with distinct cellular compositions (or laminae). Computational techniques for delineating tissue types within the hippocampus incorporate sophisticated computer vision and medical imaging algorithms to better describe the range of changes occurring in these structures. In parallel, improved MR scanner hardware and sequence choices including leveraging multiple contrasts of the same tissue in vivo allows for complementary imaging information of the structure, leading to a more specific and reliable characterisation of brain tissue. Despite advances in the field, several competing algorithms and techniques are used for hippocampus subfield morphometry properties such as volume and shape.This thesis will explore computational techniques for characterising human brain tissue in vivo using multi-contrast, high resolution, ultra high-field MRI data. Specifically, this thesis will focus on characterising the tissue of the hippocampus as an exemplar for the heterogeneity of tissue classes, complex shape changes, and large, variable individual differences in anatomy between healthy and diseased/disordered subjects. This thesis will pay specific attention to characterising hippocampus tissue (in particular, image segmentation and volumetry) in a wide range of subjects and patients, which is often ignored in computational techniques for analysing MRI data.First, techniques for retrospectively ameliorating within-session motion in MRI in both healthy participants and patients with Motor Neurone Disease (MND) will be examined and discussed. It was found that incorporating a computational technique for image registration improved the reliability of image segmentation of the subfields of the hippocampus.Next, in order to better characterise the changes in anatomy over time using MRI, we constructed and evaluated a longitudinal pipeline for segmenting and examining the volume of hippocampal subfields using computational techniques in a healthy participant pool and patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI). To aid in this research, the Towards Optimising MRI ChAracterisation of Tissue (TOMCAT) data set was acquired using Ultra-High Field (7-Tesla) MRI. Thirdly, to better characterise the subfields of the hippocampus and in less time than traditional computational techniques, a deep learning approach to hippocampus subfield segmentation using multi-contrast, high-resolution MRI was developed and evaluated. We found that deep learning techniques may aid in speeding the process of hippocampus subfield segmentation and deliver accurate results.Finally, the clinical applications of the previous studies were explored. We detail automated methods for extracting precise measures describing hippocampal subfield morphology and compare these features in MND patients and healthy controls. Brain tissue can be successfully characterised in vivo using high resolution and multi-contrast MRI. Developing robust and reproducible methods that are useful in both research and clinical contexts is an important step in demystifying biological processes involved in ageing and contributing to disorder and disease. This thesis adds to and improves the broader knowledge base of computational techniques for characterising brain tissue and adds novel and enhanced methodology to the field of neuroimaging.