A wavelet scattering convolutional network for magnetic resonance spectroscopy signal quantitation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F21%3A00544182" target="_blank" >RIV/68081731:_____/21:00544182 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26220/21:PU140482

Výsledek na webu

<a href="https://www.scitepress.org/Link.aspx?doi=10.5220/0010318502680275" target="_blank" >https://www.scitepress.org/Link.aspx?doi=10.5220/0010318502680275</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0010318502680275" target="_blank" >10.5220/0010318502680275</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A wavelet scattering convolutional network for magnetic resonance spectroscopy signal quantitation

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A wavelet scattering convolutional network for magnetic resonance spectroscopy signal quantitation

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies

ISBN

978-989-758-490-9

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

(2021)

Název nakladatele

SciTePress

Místo vydání

Setúbal

Místo konání akce

online

Datum konání akce

11. 2. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000664110100031