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%2F68081731%3A_____%2F21%3A00544182" target="_blank" >RIV/68081731:_____/21:00544182 - isvavai.cz</a>

Alternative codes found

RIV/00216305:26220/21:PU140482

Result on the web

<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>

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. 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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20601 - Medical engineering

Project

—

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

ISBN

978-989-758-490-9

ISSN

—

e-ISSN

—

Number of pages

8

Pages from-to

(2021)

Publisher name

SciTePress

Place of publication

Setúbal

Event location

online

Event date

Feb 11, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000664110100031