0557881 - ÚPT 2023 RIV CA eng C - Conference Paper (international conference)
Venglovskyi, Iurii
Magnetic Resonance Spectroscopy Signal Analysis Based on Fingerprinting Dictionary Approaches.
Proceedings of the 7th World Congress on Electrical Engineering and Computer Systems and Science (EECSS’21). Orléans: International ASET, 2021, č. článku ICBES114. ISBN 978-1-927877-92-0.
[International Conference on Biomedical Engineering and Systems (ICBES’21) /8./. online (CZ), 29.07.2021-31.07.2021]
R&D Projects: GA MŠMT(CZ) EF16_013/0001775
EU Projects: European Commission(XE) 813120 - INSPiRE-MED
Institutional support: RVO:68081731
Keywords : magnetic resonance spectroscopy * fingerprinting dictionary * spectroscopic imaging * artificial intelligence
OECD category: Medical engineering
Result website:
https://avestia.com/EECSS2021_Proceedings/files/paper/ICBES/ICBES_114.pdf

DOI: https://doi.org/10.11159/icbes21.114

Magnetic resonance spectroscopy (MRS) is a technique applicable in medical diagnosis or research, which has the unique capability to give non-invasive access to the biochemical content (metabolites) of scanned organs. Up to recent times, all the proposed methods solved metabolite quantification as an optimization problem attempting to minimize the difference between the data and a given parameterized model function. This paper proposes quantification of metabolites in MR spectroscopic imaging using a fingerprinting method, whose function is based on the creation of a dictionary of linear combinations of metabolite signals. Experimental results demonstrate the accuracy of the proposed method, compared to data obtained by a standard quantification method (QUEST), on concentration estimates of 8 metabolites from signals with macromolecule background and noise. The prototype results indicate that the concept of MR fingerprinting dictionary, useful also for preparing data for machine learning, can serve as an alternative method for metabolite quantification by NMR signal analysis.
Permanent Link: http://hdl.handle.net/11104/0331780