Deep learning for magnetic resonance spectroscopy: a time-frequency analysis approach

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F20%3APU137021" target="_blank" >RIV/00216305:26220/20:PU137021 - isvavai.cz</a>
Result on the web
<a href="http://apps.webofknowledge.com/full_record.do?product=WOS&search_mode=GeneralSearch&qid=3&SID=C37QBScKbtCLTGKNC4k&page=1&doc=1" target="_blank" >http://apps.webofknowledge.com/full_record.do?product=WOS&search_mode=GeneralSearch&qid=3&SID=C37QBScKbtCLTGKNC4k&page=1&doc=1</a>
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Deep learning for magnetic resonance spectroscopy: a time-frequency analysis approach
Original language description
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.
Czech name
—
Czech description
—

Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceeding 2 of 26th Conference student EEICT 2020
ISBN
978-80-214-5868-0
ISSN
—
e-ISSN
—
Number of pages
5
Pages from-to
131-135
Publisher name
Brno university of technology
Place of publication
Brno
Event location
BRNO
Event date
Apr 23, 2020
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
000598376500032

Similar results(10)