Deep learning for magnetic resonance spectroscopy quantification: A time frequency analysis approach
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F20%3A00540863" target="_blank" >RIV/68081731:_____/20:00540863 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Deep learning for magnetic resonance spectroscopy quantification: A time frequency analysis approach
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10301 - Atomic, molecular and chemical physics (physics of atoms and molecules including collision, interaction with radiation, magnetic resonances, Mössbauer effect)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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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Number of pages
5
Pages from-to
131-135
Publisher name
UNIV TECHNOLOGY, FAC ELECTRICAL ENG & COMMUNICATION
Place of publication
Brno
Event location
Brno
Event date
Apr 23, 2020
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
000598376500032