Quantitative Susceptibility Mapping in Human Brain: Methods Development and Applications

  • Author / Creator
    Sun, Hongfu
  • Quantitative susceptibility mapping (QSM) is an emerging magnetic resonance imaging (MRI) method that provides image contrast based on an important underlying brain tissue property. It is derived from the phase images from a gradient echo sequence, and overcomes the orientation dependency problem associated with phase imaging. However, QSM from gradient echo phase involves complicated image processing for reconstruction. This thesis explores technical challenges in QSM and provides advanced methods to solve them. Methods are introduced for background phase removal and fast QSM and then applied in three QSM applications: functional MRI studies, validation of QSM for deep grey matter iron in multiple sclerosis subjects and evaluation of QSM in patients with intracranial hemorrhage. One of the biggest challenges in QSM reconstruction is the removal of background phase. A novel method that makes use of the harmonic property of background field and Tikhonov regularization is presented in Chapter 2. The method is named RESHARP (Regularized Enabled Sophisticated Harmonic Artifact Reduction for Phase data). It is shown to be effective and robust in removal background phase while reserving local phase contrast. QSM has been proposed as a direct brain iron mapping technique for deep grey matter. However, most of the susceptibility to iron correlations are estimated using a brain iron study more than 50 years ago. A postmortem study is performed by measuring brain iron levels using Perls’ ferric iron staining and comparing with susceptibilities in multiple sclerosis brain, which is presented in Chapter 3. High linear correlations between Perls’ optical density and QSM were found in three subjects studied, leading to the conclusion that ferritin-iron is the main susceptibility source in deep GM which can be measured with QSM. Fast acquisition of QSM is also demonstrated in Chapter 4 using high resolution single-shot gradient echo-planar imaging (EPI). It reduces scan time from using regular gradient echo imaging of ~ 6mins to only 7 secs. Deep grey matter iron contrasts using EPI are found to be similar to traditional full scan. As an application of fast QSM with EPI, QSM extraction from regular fMRI studies is illustrated in Chapter 5, which also use gradient EPI. A single mean QSM from fMRI time series is derived for deep grey matter, which enables QSM application from any standard fMRI study. Heme-iron is highly concentrated in intracranial hemorrhage and changes its form with blood degradation, which makes it a perfect candidate for QSM application. However, gradient echo images in the clinic typically are obtained from a single echo with long echo time, which impedes QSM due to the fast signal decay within and around hemorrhage. A new method is presented in Chapter 6 that isolates the ICH dipole field followed by susceptibility superposition using multiple boundaries for background field removal. This method significantly reduces artifacts and makes susceptibility measurement of ICH feasible. In conclusion, this thesis has proposed methods to solve QSM reconstruction challenges, illustrated and validated its clinical value and power as a new contrast mechanism for MRI.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Doctor of Philosophy
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Biomedical Engineering
  • Supervisor / co-supervisor and their department(s)
    • Wilman, Alan H (Biomedical Engineering)
  • Examining committee members and their departments
    • Wang, Yi (Biomedical Engineering, Cornell University)
    • De Zanche, Nicola (Oncology)
    • Yahya, Atiyah (Oncology)
    • Thompson, Richard (Biomedical Engineering)
    • Wachowicz, Keith (Oncology)
    • Wilman, Alan H (Biomedical Engineering)