A Wavelet Scattering Convolutional Network for Magnetic Resonance Spectroscopy Signal Quantitation

Shamaei, Amirmohammad; Starčuková, Jana; Starčuk, Zenon

A Wavelet Scattering Convolutional Network for Magnetic Resonance Spectroscopy Signal Quantitation

Files

103185.pdf(635.94 KB)

Date

2021-02-13

Authors

Shamaei, Amirmohammad

Starčuková, Jana

Starčuk, Zenon

Publisher

Science and Technology Publications

Altmetrics

Abstract

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.

Keywords

Magnetic Resonance Spectroscopy, Quantification, Deep Learning, Machine Learning.

Citation

Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - (Volume 4). 2021, p. 268-275.
https://www.scitepress.org/PublicationsDetail.aspx?ID=gelMvIsqMOc=&t=1

Document type

Peer-reviewed

Document version

Published version

Language of document

en

Document licence

Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/