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Spinal cord diffusion imaging: Challenging characterization and prognostic value

Schneider, T; (2013) Spinal cord diffusion imaging: Challenging characterization and prognostic value. Doctoral thesis , UCL (University College London). Green open access

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Abstract

The aim of this thesis is to explore the potential of quantitative imaging mark¬ers derived from diffusion-weighted MRI (DW MRI) in the spinal cord to char¬acterise healthy white matter pathways and provide sensitivity to axonal dam¬age, regeneration and collateral sprouting in spinal cord disease. With new innovative treatment strategies emerging for spinal cord patholo¬gies such as spinal cord injury and Multiple Sclerosis, there is a need for new in-vivo biomarkers that can be specific to structural and functional changes and their underlying mechanisms on a microscopic scale. DW MRI has the potential to quantifying those microstructural characteristics beyond the scale of conventional MRI. In the first part of this dissertation I investigate Diffusion Tensor Imaging (DTI), which is the most established DW MRI analysis technique in clinical practice. In two studies we assess DTI in the context of spinal cord imaging. In the first experiment I show that DTI is sensitive to the presence of collateral fi¬bres, e.g., at inter-vertebral level where peripheral nerves enter the spinal tract. In the second experiment I propose a new method for reducing partial volume effects on whole cord DTI measurements, which is specifically tailored for the imaging and analysis challenges in the cord. The second part of this thesis comprises two studies of q-space imaging (QSI) in healthy controls. In theory, QSI offers a more comprehensive descrip¬tion of the diffusion process, but is challenging to set up on a clinical MRI scanner. I present here two QSI protocols, set up for two different scanners with different gradient hardware, receive coils and software limitations. For the first time we perform a systematic study of QSI that assesses the reproducibility and specificity to different white pathways in-vivo in the cervical cord within a group of healthy volunteers. Both studies show superior reproducibility of QSI over conventional analysis, although the results of using QSI parameters to distinguish individual white matter tracts in the cord were inconclusive. The third part of this thesis describes a new imaging method protocol based on the ActiveAx optimisation framework. It uses a complex multi- compartment model, which relates DW MRI data to microstructural parame¬ters like axon diameter and density. I design a new orientation aware method based ActiveAx, which incorporates the known fibre structure of the spinal cord. In a first step I validate the approach in in a post-mortem cervical spinal cord sample of a velveteen monkey. I then demonstrate clinical feasibility and good reproducibility of the new protocol for in-vivo human studies, using the corpus callosum as a preliminary model system for structures with uni¬directional fibre architecture. Finally I present first estimation results of axon diameter and density of the cervical spinal cord in-vivo in a healthy control that agree with the findings in the ex-vivo monkey spinal cord sample.

Type: Thesis (Doctoral)
Title: Spinal cord diffusion imaging: Challenging characterization and prognostic value
Open access status: An open access version is available from UCL Discovery
Language: English
Keywords: DTI, DWI, Microstructure, spinal cord, MRI, diffusion
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neuroinflammation
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1389063
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