Deep Learning For Magnetic Resonance Spectroscopy Quantification: A Time-Frequency Analysis Approach

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but.event.date23.04.2020cs
but.event.titleStudent EEICT 2020cs
dc.contributor.authorShamaei, Amirmohammad
dc.date.accessioned2021-07-15T13:12:39Z
dc.date.available2021-07-15T13:12:39Z
dc.date.issued2020cs
dc.description.abstractMagnetic resonance spectroscopy (MRS) is a technique capable of detecting chemical compounds from localized volumes in living tissues. Quantification of MRS signals is required for obtaining the metabolite concentrations of the tissue under investigation. However, reliable quantification of MRS is difficult. Recently deep learning (DL) has been used for metabolite quantification of MRS signals in the frequency domain. In another study, it was shown that DL in combination with time-frequency analysis could be used for artifact detection in MRS. In this study, we verify the hypothesis that DL in combination with time-frequency analysis can also be used for metabolite quantification and yields results more robust than DL trained with MR signals in the frequency domain. We used the complex matrix of absolute wavelet coefficients (WC) for the timefrequency representation of the signal, and convolutional neural network (CNN) implementation for DL. The comparison with DL used for quantification of data in the frequency domain is presented.en
dc.formattextcs
dc.format.extent131-135cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings II of the 26st Conference STUDENT EEICT 2020: Selected Papers. s. 131-135. ISBN 978-80-214-5868-0cs
dc.identifier.isbn978-80-214-5868-0
dc.identifier.urihttp://hdl.handle.net/11012/200638
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings II of the 26st Conference STUDENT EEICT 2020: Selected papersen
dc.relation.urihttps://conf.feec.vutbr.cz/eeict/EEICT2020cs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectmagnetic resonance spectroscopy; quantification; deep learning; machine learningen
dc.titleDeep Learning For Magnetic Resonance Spectroscopy Quantification: A Time-Frequency Analysis Approachen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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