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Original title:
Deep learning for magnetic resonance spectroscopy quantification: A time frequency analysis approach
Authors: Shamaei, Amirmohammad
Document type: Papers
Conference/Event: Annual Conference on Student Electrical Engineering, Information Science and Communication Technologies (STUDENT EEICT) /26./, Brno (CZ), 20200423
Year: 2020
Language: eng
Abstract: 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.
Keywords: deep learning; machine learning; magnetic resonance spectroscop; quantification
Project no.: 813120
Host item entry: Proceedings II of the 26th Conference student EEICT 2020, ISBN 978-80-214-5868-0
Institution: Institute of Scientific Instruments AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2020_sbornik_2.pdf
Original record: http://hdl.handle.net/11104/0318460
Permalink: http://www.nusl.cz/ntk/nusl-438281
The record appears in these collections:
Research > Institutes ASCR > Institute of Scientific Instruments
Conference materials > Papers
Authors: Shamaei, Amirmohammad
Document type: Papers
Conference/Event: Annual Conference on Student Electrical Engineering, Information Science and Communication Technologies (STUDENT EEICT) /26./, Brno (CZ), 20200423
Year: 2020
Language: eng
Abstract: 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.
Keywords: deep learning; machine learning; magnetic resonance spectroscop; quantification
Project no.: 813120
Host item entry: Proceedings II of the 26th Conference student EEICT 2020, ISBN 978-80-214-5868-0
Institution: Institute of Scientific Instruments AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2020_sbornik_2.pdf
Original record: http://hdl.handle.net/11104/0318460
Permalink: http://www.nusl.cz/ntk/nusl-438281
The record appears in these collections:
Research > Institutes ASCR > Institute of Scientific Instruments
Conference materials > Papers
Record created 2021-03-28, last modified 2023-12-06