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A wavelet scattering convolutional network for magnetic resonance spectroscopy signal quantitation
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SYSNO ASEP 0544182 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title A wavelet scattering convolutional network for magnetic resonance spectroscopy signal quantitation Author(s) Shamaei, Amirmohammad (UPT-D)
Starčuková, Jana (UPT-D) RID, SAI, ORCID
Starčuk jr., Zenon (UPT-D) RID, ORCID, SAINumber of authors 3 Source Title Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, Biosignals, 4. - Setúbal : SciTePress, 2021 / Bracken B. ; Fred A. ; Gamboa H. - ISBN 978-989-758-490-9 Pages (2021), s. 268-275 Number of pages 8 s. Publication form Online - E Action International Conference on Bio-inspired Systems and Signal Processing /14./ Biosignals 2021, Part of the International Joint Conference on Biomedical Engineering Systems and Technologies /14./ Biostec 2021 Event date 11.02.2021 - 13.02.2021 VEvent location online Country PT - Portugal Event type WRD Language eng - English Country PT - Portugal Keywords magnetic resonance spectroscopy ; quantification ; deep learning ; machine learning Subject RIV FS - Medical Facilities ; Equipment OECD category Medical engineering R&D Projects EF16_013/0001775 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Institutional support UPT-D - RVO:68081731 UT WOS 000664110100031 EID SCOPUS 85103860078 DOI 10.5220/0010318502680275 Annotation Magnetic resonance spectroscopy (MRS) can provide quantitative information about local metabolite concentrations in living tissues, but in practice the quantification can be difficult. Recently deep learning (DL) has been used for quantification of MRS signals in the frequency domain, and DL combined with time-frequency analysis for artefact detection in MRS. The networks most widely used in previous studies were Convolutional Neural Networks (CNN). Nonetheless, the optimal architecture and hyper-parameters of the CNN for MRS are not well understood, CNN has no knowledge about the nature of the MRS signal and its training is computationally expensive. On the other hand, Wavelet Scattering Convolutional Network (WSCN) is well-understood and computationally cheap. In this study, we found that a wavelet scattering network could hopefully be also used for metabolite quantification. We showed that a WSCN could yield results more robust than QUEST (one of quantitation methods based on model fitting) and the same as a CNN while being faster, We used wavelet scattering transform to extract features from the MRS signal, and a superficial neural network implementation to predict metabolite concentrations. Effects of phase, noise, and macromolecules variation on the WSCN estimation accuracy were also investigated. Workplace Institute of Scientific Instruments Contact Martina Šillerová, sillerova@ISIBrno.Cz, Tel.: 541 514 178 Year of Publishing 2022 Electronic address https://www.scitepress.org/Link.aspx?doi=10.5220/0010318502680275
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