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

Result code in 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>
Result on the web
<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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Result language
angličtina
Original language name
Frequency and phase shifts correction of MR spectra using deep learning in time domain
Original language description
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.
Czech name
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Czech description
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Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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