A Wavelet Scattering Convolutional Network for Magnetic Resonance Spectroscopy Signal Quantitation

A Wavelet Scattering Convolutional Network for Magnetic Resonance Spectroscopy Signal Quantitation

Stať ve sborníku v databázi WoS či Scopus

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.

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.

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

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

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

2021

13.02.2021

Science and Technology Publications

Portugal

978-989-758-490-9

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

268

275

8

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

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

103185