Název: Deep learning for magnetic resonance spectroscopy quantification: A time frequency analysis approach
Autoři: Shamaei, Amirmohammad
Typ dokumentu: Příspěvky z konference
Konference/Akce: Annual Conference on Student Electrical Engineering, Information Science and Communication Technologies (STUDENT EEICT) /26./, Brno (CZ), 20200423
Rok: 2020
Jazyk: eng
Abstrakt: 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.
Klíčová slova: deep learning; machine learning; magnetic resonance spectroscop; quantification
Číslo projektu: 813120
Zdrojový dokument: Proceedings II of the 26th Conference student EEICT 2020, ISBN 978-80-214-5868-0

Instituce: Ústav přístrojové techniky AV ČR (web)
Informace o dostupnosti dokumentu: Dokument je dostupný na externích webových stránkách.
Externí umístění souboru: https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2020_sbornik_2.pdf
Původní záznam: http://hdl.handle.net/11104/0318460

Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-438281


Záznam je zařazen do těchto sbírek:
Věda a výzkum > AV ČR > Ústav přístrojové techniky
Konferenční materiály > Příspěvky z konference