Magnetic resonance imaging (MRI) is a highly
exible and non-invasive medical imaging
modality based on the concept of nuclear magnetic resonance (NMR). Compared to other
imaging techniques, major limitation of MRI is the relatively long acquisition time. The
slowness of acquisition makes MRI difficult to apply to time-sensitive clinical applications.
Acquisition of MRA images with a spatial resolution close to conventional digital subtraction
angiography is feasible, but at the expense of reduction in temporal resolution. Parallel
MRI employs multiple receiver coils to speed up the MRI acquisition by reducing the
number of data points collected. Although, the reconstructed images from undersampled
data sets often suffer from different different types of degradation and artifacts.
In contrast-enhanced magnetic resonance imaging, information is effectively measured in
3D k-space one line at a time therefore the 3D data acquisition extends over several minutes
even using parallel receiver coils. This limits the assessment of high
ow lesions and some
vascular tumors in patients. To improve spatio-temporal resolution in contrast enhanced
magnetic resonance angiography (CE-MRA), the use of incorporating prior knowledge in
the image recovery process is considered in this thesis.
There are five contributions in this thesis. The first contribution is the modification
of generalized unaliasing using support and sensitivity encoding (GUISE). GUISE was
introduced by this group to explore incorporating prior knowledge of the image to be
reconstructed in parallel MRI. In order to provide improved time-resolved MRA image
sequences of the blood vessels, the GUISE method requires an accurate segmentation
of the relatively noisy 3D data set into vessel and background. The method that was
originally used for definition of the effective region of support was primitive and produced
a segmented image with much false detection because of the effect of overlying structures
and the relatively noisy background in images. We proposed to use the statistical principle
as employed for the modified maximum intensity projection (MIP) to achieve better 3D
segmentation and optimal visualization of blood vessels. In comparison with the previous
region of support (ROS), the new one enables higher accelerations MRA reconstructions
due to the decreased volume of the ROS and leads to less computationally expensive
reconstruction.
In the second contribution we demonstrated the impact of imposing the Karhunen-Loeve transform (KLT) basis for the temporal changes, based on prior expectation of the changes
in contrast concentration with time. In contrast with other transformation, KLT of the
temporal variation showed a better contrast to noise ratio (CNR) can be achieved.
By incorporating a data ordering step with compressed sensing (CS), an improvement
in image quality for reconstructing parallel MR images was exhibited in prior estimate
based compressed sensing (PECS). However, this method required a prior estimate of
the image to be available. A singular value decomposition (SVD) modification of PECS
algorithm (SPECS) to explore ways of utilising the data ordering step without requiring
a prior estimate was extended as the third contribution. By employing singular value
decomposition as the sparsifying transform in the CS algorithm, the recovered image was
used to derive the data ordering in PECS. The preliminary results outperformed the PECS
results.
The fourth contribution is a novel approach for training a dictionary for sparse recovery
in CE-MRA. The experimental results demonstrate improved reconstructions on clinical
undersampled dynamic images.
A new method recently has been developed to exploit the structure of the signal in sparse
representation. Group sparse compressed sensing (GSCS) allows the efficient reconstruction
of signals whose support is contained in the union of a small number of groups (sets)
from a collection of pre-defined disjoint groups. Exploiting CS applications in dynamic
MR imaging, a group sparse method was introduced for our contrast-enhanced data set.
Instead of incorporating data ordering resulted from prior information, pre-defined sparsity
patterns were used in the PECS recovery algorithm, resulting to a suppression of noise in
the reconstruction.
| Identifer | oai:union.ndltd.org:canterbury.ac.nz/oai:ir.canterbury.ac.nz:10092/9332 |
| Date | January 2014 |
| Creators | Vafadar, Bahareh |
| Publisher | University of Canterbury. Electrical and Computer Engineering |
| Source Sets | University of Canterbury |
| Language | English |
| Detected Language | English |
| Type | Electronic thesis or dissertation, Text |
| Rights | Copyright Bahareh Vafadar, http://library.canterbury.ac.nz/thesis/etheses_copyright.shtml |
| Relation | NZCU |
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