Model-based myelin water fraction mapping: analyses and improvement

In this thesis, the problem of model-based myelin water fraction (MWF) mapping is addressed. We first focus on three of the most widely used signal models for T2*-myelin water imaging (MWI), i.e., the NNLS-multi-exponential model, the magnitude-3-exponential model, and the complex-3-exponential model, and investigate their sensitivities to practical perturbations such as random noise and field-related structured errors. We demonstrate through both Cramér-Rao lower bound (CRLB) analyses and Monte Carlo simulations that the three signal models are all very unstable inherently. Comparatively speaking, however, we demonstrate the theoretical advantage of the 3-exponential models over the multi-exponential model in handling noise, and the practical advantage of the magnitude models over the complex model in handling phase-related perturbations for T2*-MWI. We also illustrate the necessity and effects of incorporating various types of constraints for additional sensitivity gain. Using the insights obtained in the sensitivity analyses, we then propose a new MWF fitting scheme that leverages an improved signal model and a set of more effective constraints. In particular, a relaxed magnitude-3-exponential model with additional frequency compensation terms is introduced to better represent voxels with large field variations; a set of statistical distributions learned from in vivo training data is further imposed on the model parameters for additional constraints. Using phantom simulation and in vivo experiments, we then evaluate and compare the proposed method with several popular conventional MWF fitting schemes to demonstrate the improved accuracy and robustness of the proposed method. In this thesis, a literature review on the study of myelin and the development of MWF mapping is provided at the start of the work. Background materials on the CRLB theories are also provided to facilitate reading.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2021

Author dc:creator

• Xiong, Jiahui

Contributors dc:contributor

• Liang, Zhi-Pei

• myelin water fraction
• sensitivity analysis
• Bayesian estimation
• Cramér-Rao lower bound

Statement dc:rights

• Copyright 2020 Jiahui Xiong

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/109583

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/109583