Frequency and phase shifts correction of MR spectra using deep learning in time domain

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU144222" target="_blank" >RIV/00216305:26220/21:PU144222 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s10334-021-00947-8" target="_blank" >https://link.springer.com/article/10.1007/s10334-021-00947-8</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Frequency and phase shifts correction of MR spectra using deep learning in time domain

Popis výsledku v původním jazyce

Processing magnetic resonance spectroscopy (MRS) signals remains challenging due to hardware and physiologic processes, which may lead to frequency and phase shifts (FPS). Thus, frequency-and-phase correction (FPC) is a useful step in MRS signal processing. Deep learning (DL) has proved to be successful in a wide range of tasks, including the MR field. DL applications in MRS have recently emerged1 . It has been shown that DL can also be used for FPC2 in the frequency domain with two separated networks. In this study, we proposed a novel deep autoencoder (DAE) network for FPC. We showed that a single DAE network could learn a nonlinear low-dimensional model to predict frequency and phase shifts.

Název v anglickém jazyce

Frequency and phase shifts correction of MR spectra using deep learning in time domain

Popis výsledku anglicky

Processing magnetic resonance spectroscopy (MRS) signals remains challenging due to hardware and physiologic processes, which may lead to frequency and phase shifts (FPS). Thus, frequency-and-phase correction (FPC) is a useful step in MRS signal processing. Deep learning (DL) has proved to be successful in a wide range of tasks, including the MR field. DL applications in MRS have recently emerged1 . It has been shown that DL can also be used for FPC2 in the frequency domain with two separated networks. In this study, we proposed a novel deep autoencoder (DAE) network for FPC. We showed that a single DAE network could learn a nonlinear low-dimensional model to predict frequency and phase shifts.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

20601 - Medical engineering

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů