Publication detail

DEEP LEARNING FOR MAGNETIC RESONANCE SPECTROSCOPY QUANTIFICATION: A TIME-FREQUENCY ANALYSIS APPROACH

SHAMAEI, A.

Original Title

DEEP LEARNING FOR MAGNETIC RESONANCE SPECTROSCOPY QUANTIFICATION: A TIME-FREQUENCY ANALYSIS APPROACH

Type

conference paper

Language

English

Original Abstract

In this study, we verify the hypothesis that deep learning in combination with time-frequency analsis can be used for metabolite quantification and yeilds results more robust than deep learning trained with magnetic resonance signals in the frequency domain.

Keywords

magnetic resonance spectroscopy ,quantification, deep learning, machine learning

Authors

SHAMAEI, A.

Released

23. 4. 2020

Publisher

Brno university of technology

Location

Brno

ISBN

978-80-214-5868-0

Book

Proceeding 2 of 26th Conference student EEICT 2020

Edition

1

Edition number

2

Pages from

131

Pages to

135

Pages count

5

URL

https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2020_sbornik_2.pdf

BibTex

@inproceedings{BUT164779,
  author="Amirmohammad {Shamaei}",
  title="DEEP LEARNING FOR MAGNETIC RESONANCE SPECTROSCOPY QUANTIFICATION: A TIME-FREQUENCY ANALYSIS APPROACH",
  booktitle="Proceeding 2 of 26th Conference student EEICT 2020",
  year="2020",
  series="1",
  number="2",
  pages="131--135",
  publisher="Brno university of technology",
  address="Brno",
  isbn="978-80-214-5868-0",
  url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2020_sbornik_2.pdf"
}