Magnetic resonance imaging (MRI) is the preferred imaging modality for visualization
of intracranial soft tissues. Surgical planning, and increasingly surgical navigation, use
high resolution 3-D patient-specific structural maps of the brain. However, the process of
MRI is a multi-parameter tomographic technique where high resolution imagery competes
against high contrast and reasonable acquisition times.
Resolution enhancement techniques based on super-resolution are particularly well suited
in solving the problems of resolution when high contrast with reasonable times for
MRI acquisitions are needed. Super-resolution is the concept of reconstructing a high resolution
image from a set of low-resolution images taken at dierent viewpoints or foci. The
MRI encoding techniques that produce high resolution imagery are often sub-optimal for
the desired contrast needed for visualization of some structures in the brain.
A novel super-resolution reconstruction framework for MRI is proposed in this thesis.
Its purpose is to produce images of both high resolution and high contrast desirable for
image-guided minimally invasive brain surgery. The input data are multiple 2-D multi-slice
Inversion Recovery MRI scans acquired at orientations with regular angular spacing rotated
around a common axis. Inspired by the computed tomography domain, the reconstruction is
a 3-D volume of isotropic high resolution, where the inversion process resembles a projection
reconstruction problem. Iterative algorithms for reconstruction are based on the projection
onto convex sets formalism. Results demonstrate resolution enhancement in simulated
phantom studies, and in ex- and in-vivo human brain scans, carried out on clinical scanners.
In addition, a novel motion correction method is applied to volume registration using an
iterative technique in which super-resolution reconstruction is estimated in a given iteration
following motion correction in the preceding iteration. A comparison study of our method
with previously published methods in super-resolution shows favorable characteristics of the
proposed approach.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/33807 |
| Date | 02 April 2009 |
| Creators | Shilling, Richard Zethward |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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