A Wavelet Scattering Convolutional Network for Magnetic Resonance Spectroscopy Signal Quantitation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU140482" target="_blank" >RIV/00216305:26220/21:PU140482 - isvavai.cz</a>
Alternative codes found
RIV/68081731:_____/21:00544182
Result on the web
<a href="https://www.scitepress.org/PublicationsDetail.aspx?ID=gelMvIsqMOc=&t=1" target="_blank" >https://www.scitepress.org/PublicationsDetail.aspx?ID=gelMvIsqMOc=&t=1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0010318502680275" target="_blank" >10.5220/0010318502680275</a>

Result language
angličtina
Original language name
A Wavelet Scattering Convolutional Network for Magnetic Resonance Spectroscopy Signal Quantitation
Original language description
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.
Czech name
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Czech description
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Project
<a href="/en/project/EF16_013%2F0001775" target="_blank" >EF16_013/0001775: Modernization and support of research activities of the national infrastructure for biological and medical imaging Czech-BioImaging</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - (Volume 4)
ISBN
978-989-758-490-9
ISSN
—
e-ISSN
—
Number of pages
8
Pages from-to
268-275
Publisher name
Science and Technology Publications
Place of publication
Portugal
Event location
Vienna
Event date
Feb 11, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000664110100031

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