Application of Compressed Sensing to Single Voxel J Resolved Magnetic Resonance Spectroscopy: Simulation and In Vitro Results

Geraghty, Benjamin

Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/13128

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DC Field	Value	Language
dc.contributor.advisor	Noseworthy, Michael	en_US
dc.contributor.author	Geraghty, Benjamin	en_US
dc.date.accessioned	2014-06-18T17:02:36Z	-
dc.date.available	2014-06-18T17:02:36Z	-
dc.date.created	2013-04-29	en_US
dc.date.issued	2013	en_US
dc.identifier.other	opendissertations/7954	en_US
dc.identifier.other	8857	en_US
dc.identifier.other	4086435	en_US
dc.identifier.uri	http://hdl.handle.net/11375/13128	-
dc.description	<p>Localized Magnetic Resonance Spectroscopy is a non-invasive tool that offers insight into physiological status via signals arising from biological compounds. Unambiguous evaluation of said signals, however; is intrinsically limited by self interference through signal overlap. J Resolved Spectroscopy introduces an additional dimension to the measured signal which reduces overlap at the cost of increasing the scan duration. Compressed Sensing is a growing mathematical framework that asserts that under certain conditions, if a signal admits a sparse representation then it can be recovered from fewer measurements than required by classical signal theory. This framework has been successfully applied in high resolution Nuclear Magnetic Resonance experiments, justifying the investigation into its applicability in the realm of localized Magnetic Resonance Spectroscopy. The problem is addressed by optimizing the Compressed Sensing recovery on model spectra and evaluated in vitro through a parametric approach.</p>	en_US
dc.description.abstract	<p>Localized Magnetic Resonance Spectroscopy is a non-invasive tool that o↵ers insight into physiological status via signals arising from biological compounds. Unambiguous evaluation of said signals, however; is intrinsically limited by self interference through signal overlap. J Resolved Spectroscopy introduces an additional dimension to the measured signal which reduces overlap at the cost of increasing the scan duration. Compressed Sensing is a growing mathematical framework that asserts that under certain conditions, if a signal admits a sparse representation then it can be recovered from fewer measurements than required by classical signal theory. This framework has been successfully applied in high resolution Nuclear Magnetic Resonance experiments, justifying the investigation into its applicability in the realm of localized Magnetic Resonance Spectroscopy. The problem is addressed by optimizing the Compressed Sensing recovery on model spectra and evaluated in vitro through a parametric approach.</p>	en_US
dc.subject	Compressed Sensing	en_US
dc.subject	JPRESS	en_US
dc.subject	Magnetic Resonance Spectroscopy	en_US
dc.subject	ProFit	en_US
dc.subject	Electrical and Computer Engineering	en_US
dc.subject	Electrical and Computer Engineering	en_US
dc.title	Application of Compressed Sensing to Single Voxel J Resolved Magnetic Resonance Spectroscopy: Simulation and In Vitro Results	en_US
dc.type	thesis	en_US
dc.contributor.department	Electrical and Computer Engineering	en_US
dc.description.degree	Master of Applied Science (MASc)	en_US
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