Motion -Compensated 3-D Generalized Series Imaging

Three-dimensional magnetic resonance (MR) imaging is highly desirable from the standpoints of minimum voxel size, volumetric coverage and signal-to-noise efficiency. Unfortunately, long scan times have traditionally precluded the use of 3-D imaging in applications where simultaneous resolution of both small spatial structures and closely spaced temporal events is important. The research in this thesis is aimed at exploiting the generalized series (GS) framework to improve imaging efficiency for 3-D dynamic MR imaging. Several data acquisition, sampling and modeling techniques for time-resolved volumetric imaging are proposed and analyzed. The research also includes a characterization of the effects of object motion in GS imaging. It is shown that, unlike other constrained dynamic imaging methods, measured data can be retrospectively processed to correct for motion after the experiment. This asset is especially important for 3-D imaging, where scan times may be long even when the GS approach is used to reduce the number of measurements. Based on the assumption of rigid-body displacement, two motion compensation techniques are developed to reduce the sensitivity of the GS method to motion. Finally, computer simulation and experimental results are provided to demonstrate the practical application of the proposed motion-compensated 3-D GS imaging approach. This method should prove useful for a number of applications, including contrast-enhanced MR mammography, dynamic MR angiography, monitoring of interventional procedures and functional brain studies.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Hess, Christopher Paul

Contributors dc:contributor

• Liang, Zhi-Pei

• Health Sciences, Radiology

Language dc:language

eng

Identifier

(MiAaPQ)AAI9953044

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81308