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

dc.contributor.advisorNoseworthy, Michaelen_US
dc.contributor.authorGeraghty, Benjaminen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.date.accessioned2014-06-18T17:02:36Z
dc.date.available2014-06-18T17:02:36Z
dc.date.created2013-04-29en_US
dc.date.issued2013en_US
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.description.degreeMaster of Applied Science (MASc)en_US
dc.identifier.otheropendissertations/7954en_US
dc.identifier.other8857en_US
dc.identifier.other4086435en_US
dc.identifier.urihttp://hdl.handle.net/11375/13128
dc.subjectCompressed Sensingen_US
dc.subjectJPRESSen_US
dc.subjectMagnetic Resonance Spectroscopyen_US
dc.subjectProFiten_US
dc.subjectElectrical and Computer Engineeringen_US
dc.subjectElectrical and Computer Engineeringen_US
dc.titleApplication of Compressed Sensing to Single Voxel J Resolved Magnetic Resonance Spectroscopy: Simulation and In Vitro Resultsen_US
dc.typethesisen_US

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