Publication detail

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

SHAMAEI, A. STARČUKOVÁ, J. STARČUK, Z.

Original Title

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

Type

journal article in Web of Science

Language

English

Original Abstract

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.

Keywords

MR spectroscopy; Inverse problem; Deep learning; Machine learning; Convolutional neural network; Metabolite quantification

Authors

SHAMAEI, A.; STARČUKOVÁ, J.; STARČUK, Z.

Released

5. 4. 2023

Publisher

Elsevier

ISBN

0010-4825

Periodical

COMPUTERS IN BIOLOGY AND MEDICINE

Year of study

158

Number

1

State

United States of America

Pages from

1

Pages to

15

Pages count

15

URL

https://www.sciencedirect.com/science/article/pii/S0010482523003025

Full text in the Digital Library

http://hdl.handle.net/11012/213625

BibTex

@article{BUT183318,
  author="Amirmohammad {Shamaei} and Jana {Starčuková} and Zenon {Starčuk}",
  title="Physics-informed deep learning approach to quantification of human brain metabolites from magnetic resonance spectroscopy data",
  journal="COMPUTERS IN BIOLOGY AND MEDICINE",
  year="2023",
  volume="158",
  number="1",
  pages="1--15",
  doi="10.1016/j.compbiomed.2023.106837",
  issn="0010-4825",
  url="https://www.sciencedirect.com/science/article/pii/S0010482523003025"
}