Physics-informed deep learning approach to quantification of human brain metabolites from magnetic resonance spectroscopy data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F23%3A00570880" target="_blank" >RIV/68081731:_____/23:00570880 - isvavai.cz</a>

Alternative codes found

RIV/00216305:26220/23:PU148010

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0010482523003025" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0010482523003025</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.compbiomed.2023.106837" target="_blank" >10.1016/j.compbiomed.2023.106837</a>

Result language

angličtina

Original language name

Physics-informed deep learning approach to quantification of human brain metabolites from magnetic resonance spectroscopy data

Original language description

Purpose: While the recommended analysis method for magnetic resonance spectroscopy data is linear combination model (LCM) fitting, the supervised deep learning (DL) approach for quantification of MR spectroscopy (MRS) and MR spectroscopic imaging (MRSI) data recently showed encouraging results, however, supervised learning requires ground truth fitted spectra, which is not practical. Moreover, this work investigates the feasibility and efficiency of the LCM-based self-supervised DL method for the analysis of MRS data. Method: We present a novel DL-based method for the quantification of relative metabolite concentrations, using quantum-mechanics simulated metabolite responses and neural networks. We trained, validated, and evaluated the proposed networks with simulated and publicly accessible in-vivo human brain MRS data and compared the performance with traditional methods. A novel adaptive macromolecule fitting algorithm is included. We investigated the performance of the proposed methods in a Monte Carlo (MC) study. Result: The validation using low-SNR simulated data demonstrated that the proposed methods could perform quantification comparably to other methods. The applicability of the proposed method for the quantification of in-vivo MRS data was demonstrated. Our proposed networks have the potential to reduce computation time significantly. Conclusion: The proposed model-constrained deep neural networks trained in a self-supervised manner can offer fast and efficient quantification of MRS and MRSI data. Our proposed method has the potential to facilitate clinical practice by enabling faster processing of large datasets such as high-resolution MRSI datasets, which may have thousands of spectra.

Czech name

—

Czech description

—

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

Result was created during the realization of more than one project. More information in the Projects tab.

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

Computers in Biology Medicine

ISSN

0010-4825

e-ISSN

1879-0534

Volume of the periodical

158

Issue of the periodical within the volume

May

Country of publishing house

GB - UNITED KINGDOM

Number of pages

15

Pages from-to

106837

UT code for WoS article

000982004200001

EID of the result in the Scopus database

2-s2.0-85151756081