Deep learning for magnetic resonance spectroscopy quantification: A time frequency analysis approach

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Deep learning for magnetic resonance spectroscopy quantification: A time frequency analysis approach

Popis výsledku v původním jazyce

Magnetic 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 time-frequency 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.

Název v anglickém jazyce

Deep learning for magnetic resonance spectroscopy quantification: A time frequency analysis approach

Popis výsledku anglicky

Magnetic 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 time-frequency 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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10301 - Atomic, molecular and chemical physics (physics of atoms and molecules including collision, interaction with radiation, magnetic resonances, Mössbauer effect)

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings II of the 26th Conference student EEICT 2020

ISBN

978-80-214-5868-0

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

131-135

Název nakladatele

UNIV TECHNOLOGY, FAC ELECTRICAL ENG & COMMUNICATION

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

23. 4. 2020

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

000598376500032