Model-informed unsupervised deep learning approaches to frequency and phase correction of MRS signals

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F23%3A00563909" target="_blank" >RIV/68081731:_____/23:00563909 - isvavai.cz</a>

Alternative codes found

RIV/00216305:26220/22:PU147459

Result on the web

<a href="https://onlinelibrary.wiley.com/doi/10.1002/mrm.29498" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1002/mrm.29498</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/mrm.29498" target="_blank" >10.1002/mrm.29498</a>

Result language

angličtina

Original language name

Model-informed unsupervised deep learning approaches to frequency and phase correction of MRS signals

Original language description

Purpose: A supervised deep learning (DL) approach for frequency and phase correction (FPC) of MRS data recently showed encouraging results, but obtaining transients with labels for supervised learning is challenging. This work investigates the feasibility and efficiency of unsupervised deep learning–based FPC. Methods: Two novel deep learning–based FPC methods (deep learning–based Cr referencing and deep learning–based spectral registration), which use a priori physics domain knowledge, are presented. The proposed networks were trained, validated, and evaluated using simulated, phantom, and publicly accessible in vivo MEGA-edited MRS data. The performance of our proposed FPC methods was compared with other generally used FPC methods, in terms of precision and time efficiency. A new measure was proposed in this study to evaluate the FPC method performance. The ability of each of our methods to carry out FPC at varying SNR levels was evaluated. A Monte Carlo study was carried out to investigate the performance of our proposed methods. Results: The validation using low-SNR manipulated simulated data demonstrated that the proposed methods could perform FPC comparably with other methods. The evaluation showed that the deep learning–based spectral registration over a limited frequency range method achieved the highest performance in phantom data. The applicability of the proposed method for FPC of GABA-edited in vivo MRS data was demonstrated. Our proposed networks have the potential to reduce computation time significantly. Conclusions: The proposed physics-informed deep neural networks trained in an unsupervised manner with complex data can offer efficient FPC of large MRS data in a shorter time.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Name of the periodical

Magnetic Resonance in Medicine

ISSN

0740-3194

e-ISSN

1522-2594

Volume of the periodical

89

Issue of the periodical within the volume

3

Country of publishing house

US - UNITED STATES

Number of pages

16

Pages from-to

1221-1236

UT code for WoS article

000881706800001

EID of the result in the Scopus database

2-s2.0-85141952111