Publication detail

A Wavelet Scattering Convolutional Network for Magnetic Resonance Spectroscopy Signal Quantitation

SHAMAEI, A. STARČUKOVÁ, J. STARČUK, Z.

Original Title

A Wavelet Scattering Convolutional Network for Magnetic Resonance Spectroscopy Signal Quantitation

Type

conference paper

Language

English

Original 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.

Authors

SHAMAEI, A.; STARČUKOVÁ, J.; STARČUK, Z.

Released

13. 2. 2021

Publisher

Science and Technology Publications

Location

Portugal

ISBN

978-989-758-490-9

Book

Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - (Volume 4)

Pages from

268

Pages to

275

Pages count

8

URL

https://www.scitepress.org/PublicationsDetail.aspx?ID=gelMvIsqMOc=&t=1

Full text in the Digital Library

http://hdl.handle.net/11012/200993

BibTex

@inproceedings{BUT170998,
  author="Amirmohammad {Shamaei} and Jana {Starčuková} and Zenon {Starčuk}",
  title="A Wavelet Scattering Convolutional Network for Magnetic Resonance Spectroscopy Signal Quantitation",
  booktitle="Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - (Volume 4)",
  year="2021",
  pages="268--275",
  publisher="Science and Technology Publications",
  address="Portugal",
  doi="10.5220/0010318502680275",
  isbn="978-989-758-490-9",
  url="https://www.scitepress.org/PublicationsDetail.aspx?ID=gelMvIsqMOc=&t=1"
}