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Comparison of convolutional-neural-networks-based method and LCModel on the quantification of in vivo magnetic resonance spectroscopy

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Abstract

Background

Quantification of metabolites concentrations in institutional unit (IU) is important for inter-subject and long-term comparisons in the applications of magnetic resonance spectroscopy (MRS). Recently, deep learning (DL) algorithms have found a variety of applications on the process of MRS data. A quantification strategy compatible to DL base MRS spectral processing method is, therefore, useful.

Materials and methods

This study aims to investigate whether metabolite concentrations quantified using a convolutional neural network (CNN) based method, coupled with a scaling procedure that normalizes spectral signals for CNN input and linear regression, can effectively reflect variations in metabolite concentrations in IU across different brain regions with varying signal-to-noise ratios (SNR) and linewidths (LW). An error index based on standard error (SE) is proposed to indicate the confidence levels associated with metabolite predictions. In vivo MRS spectra were acquired from three brain regions of 43 subjects using a 3T system.

Results

The metabolite concentrations in IU of five major metabolites, quantified using CNN and LCModel, exhibit similar ranges with Pearson’s correlation coefficients ranging from 0.24 to 0.78. The SE of the metabolites shows a positive correlation with Cramer-Rao lower bound (CRLB) (r=0.46) and  absolute CRLB (r=0.81), calculated by multiplying CRLBs with the quantified metabolite content.

Conclusion

In conclusion, the CNN based method with the proposed scaling procedures can be employed to quantify in vivo MRS spectra and derive metabolites concentrations in IU. The SE can be used as error index, indicating predicted uncertainties for metabolites and sharing information similar to the absolute CRLB.

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Data availability

The data cannot be shared at this time as the data also forms part of an ongoing study. Please contact the corrosponding author for futher access.

References

  1. Niddam DM, Lai KL, Tsai SY, Lin YR, Chen WT, Fuh JL, Wang SJ (2020) Brain metabolites in chronic migraine patients with medication overuse headache. Cephalalgia 40(8):851–862

    Article  PubMed  Google Scholar 

  2. Niddam DM, Wang SJ, Tsai SY (2021) Pain sensitivity and the primary sensorimotor cortices: a multimodal neuroimaging study. Pain 162(3):846–855

    Article  PubMed  Google Scholar 

  3. Niddam DM, Lai KL, Tsai SY, Lin YR, Chen WT, Fuh JL, Wang SJ (2018) Neurochemical changes in the medial wall of the brain in chronic migraine. Brain 141:377–390

    Article  PubMed  Google Scholar 

  4. Schur RR, Draisma LW, Wijnen JP, Boks MP, Koevoets MG, Joels M, Klomp DW, Kahn RS, Vinkers CH (2016) Brain GABA levels across psychiatric disorders: a systematic literature review and meta-analysis of (1) H-MRS studies. Hum Brain Mapp 37(9):3337–3352

    Article  PubMed  PubMed Central  Google Scholar 

  5. Birch R, Peet AC, Dehghani H, Wilson M (2017) Influence of macromolecule baseline on (1) H MR spectroscopic imaging reproducibility. Magn Reson Med 77(1):34–43

    Article  PubMed  Google Scholar 

  6. Tsai SY, Lin YR, Lin HY, Lin FH (2019) Reduction of lipid contamination in MR spectroscopy imaging using signal space projection. Magn Reson Med 81(3):1486–1498

    Article  CAS  PubMed  Google Scholar 

  7. Tkac I, Deelchand D, Dreher W, Hetherington H, Kreis R, Kumaragamage C, Povazan M, Spielman DM, Strasser B, de Graaf RA (2021) Water and lipid suppression techniques for advanced (1) H MRS and MRSI of the human brain: experts’ consensus recommendations. NMR Biomed 34(5):e4459

    Article  CAS  PubMed  Google Scholar 

  8. Jiru F, Skoch A, Wagnerova D, Dezortova M, Hajek M (2013) jSIPRO - analysis tool for magnetic resonance spectroscopic imaging. Comput Methods Programs Biomed 112(1):173–188

    Article  PubMed  Google Scholar 

  9. Borbath T, Murali-Manohar S, Dorst J, Wright AM, Henning A (2021) ProFit-1D-A 1D fitting software and open-source validation data sets. Magn Reson Med 86(6):2910–2929

    Article  PubMed  Google Scholar 

  10. Near J, Harris AD, Juchem C, Kreis R, Marjanska M, Oz G, Slotboom J, Wilson M, Gasparovic C (2021) Preprocessing, analysis and quantification in single-voxel magnetic resonance spectroscopy: experts’ consensus recommendations. NMR Biomed 34(5):e4257

    Article  PubMed  Google Scholar 

  11. Edden RA, Puts NA, Harris AD, Barker PB, Evans CJ (2014) Gannet: A batch-processing tool for the quantitative analysis of gamma-aminobutyric acid-edited MR spectroscopy spectra. J Magn Reson Imaging 40(6):1445–1452

    Article  PubMed  Google Scholar 

  12. Simpson R, Devenyi GA, Jezzard P, Hennessy TJ, Near J (2017) Advanced processing and simulation of MRS data using the FID appliance (FID-A)-An open source, MATLAB-based toolkit. Magn Reson Med 77(1):23–33

    Article  CAS  PubMed  Google Scholar 

  13. Provencher SW (2001) Automatic quantitation of localized in vivo 1H spectra with LCModel. NMR Biomed 14(4):260–264

    Article  CAS  PubMed  Google Scholar 

  14. Jablonski M, Starcukova J, Starcuk Z Jr (2017) Processing tracking in jMRUI software for magnetic resonance spectra quantitation reproducibility assurance. BMC Bioinformatics 18(1):56

    Article  PubMed  PubMed Central  Google Scholar 

  15. Wilson M, Reynolds G, Kauppinen RA, Arvanitis TN, Peet AC (2011) A constrained least-squares approach to the automated quantitation of in vivo (1)H magnetic resonance spectroscopy data. Magn Reson Med 65(1):1–12

    Article  CAS  PubMed  Google Scholar 

  16. Chong DG, Kreis R, Bolliger CS, Boesch C, Slotboom J (2011) Two-dimensional linear-combination model fitting of magnetic resonance spectra to define the macromolecule baseline using FiTAID, a Fitting Tool for Arrays of Interrelated Datasets. MAGMA 24(3):147–164

    Article  PubMed  Google Scholar 

  17. Maudsley AA, Domenig C, Govind V, Darkazanli A, Studholme C, Arheart K, Bloomer C (2009) Mapping of brain metabolite distributions by volumetric proton MR spectroscopic imaging (MRSI). Magn Reson Med 61(3):548–559

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Gasparovic C, Song T, Devier D, Bockholt HJ, Caprihan A, Mullins PG, Posse S, Jung RE, Morrison LA (2006) Use of tissue water as a concentration reference for proton spectroscopic imaging. Magn Reson Med 55(6):1219–1226

    Article  CAS  PubMed  Google Scholar 

  19. Lecocq A, Le Fur Y, Amadon A, Vignaud A, Cozzone PJ, Guye M, Ranjeva JP (2015) Fast water concentration mapping to normalize (1)H MR spectroscopic imaging. MAGMA 28(1):87–100

    Article  CAS  PubMed  Google Scholar 

  20. Rizzo R, Dziadosz M, Kyathanahally SP, Shamaei A, Kreis R (2023) Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias. Magn Reson Med 89(5):1707–1727

    Article  CAS  PubMed  Google Scholar 

  21. Kreis R (2016) The trouble with quality filtering based on relative Cramer-Rao lower bounds. Magn Reson Med 75(1):15–18

    Article  PubMed  Google Scholar 

  22. Kyathanahally SP, Doring A, Kreis R (2018) Deep learning approaches for detection and removal of ghosting artifacts in MR spectroscopy. Magn Reson Med 80(3):851–863

    Article  PubMed  Google Scholar 

  23. Gurbani SS, Schreibmann E, Maudsley AA, Cordova JS, Soher BJ, Poptani H, Verma G, Barker PB, Shim H, Cooper LAD (2018) A convolutional neural network to filter artifacts in spectroscopic MRI. Magn Reson Med 80(5):1765–1775

    Article  PubMed  PubMed Central  Google Scholar 

  24. Gurbani SS, Sheriff S, Maudsley AA, Shim H, Cooper LAD (2019) Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting. Magn Reson Med 81(5):3346–3357

    Article  PubMed  PubMed Central  Google Scholar 

  25. Shamaei A, Starcukova J, Starcuk Z Jr (2023) Physics-informed deep learning approach to quantification of human brain metabolites from magnetic resonance spectroscopy data. Comput Biol Med 158:106837

    Article  PubMed  Google Scholar 

  26. Lee H, Lee HH, Kim H (2020) Reconstruction of spectra from truncated free induction decays by deep learning in proton magnetic resonance spectroscopy. Magn Reson Med 84(2):559–568

    Article  CAS  PubMed  Google Scholar 

  27. Lee HH, Kim H (2019) Intact metabolite spectrum mining by deep learning in proton magnetic resonance spectroscopy of the brain. Magn Reson Med 82(1):33–48

    Article  CAS  PubMed  Google Scholar 

  28. Lee HH, Kim H (2020) Deep learning-based target metabolite isolation and big data-driven measurement uncertainty estimation in proton magnetic resonance spectroscopy of the brain. Magn Reson Med 84(4):1689–1706

    Article  CAS  PubMed  Google Scholar 

  29. Govindaraju V, Young K, Maudsley AA (2000) Proton NMR chemical shifts and coupling constants for brain metabolites. NMR Biomed 13(3):129–153

    Article  CAS  PubMed  Google Scholar 

  30. Opstad KS, Bell BA, Griffiths JR, Howe FA (2008) Toward accurate quantification of metabolites, lipids, and macromolecules in HRMAS spectra of human brain tumor biopsies using LCModel. Magn Reson Med 60(5):1237–1242

    Article  CAS  PubMed  Google Scholar 

  31. Peng PY, Tsai SY, Lin YR (2016) Toolbox for automatic localization of volume of interest in MRS (ALLVOI). Paper presented at the Proceedings of the 24th Annual Meeting of ISMRM, Singapore

  32. Park YW, Deelchand DK, Joers JM, Hanna B, Berrington A, Gillen JS, Kantarci K, Soher BJ, Barker PB, Park H, Oz G, Lenglet C (2018) AutoVOI: real-time automatic prescription of volume-of-interest for single voxel spectroscopy. Magn Reson Med 80(5):1787–1798

    Article  PubMed  PubMed Central  Google Scholar 

  33. Tsai SY, Fang CH, Wu TY, Lin YR (2016) Effects of frequency drift on the quantification of gamma-aminobutyric acid using MEGA-PRESS. Sci Rep 6:24564

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Oz G, Deelchand DK, Wijnen JP, Mlynarik V, Xin L, Mekle R, Noeske R, Scheenen TWJ, Tkac I, Experts' Working Group on Advanced Single Voxel HM (2020) Advanced single voxel(1) H magnetic resonance spectroscopy techniques in humans: experts' consensus recommendations. NMR Biomed. https://doi.org/10.1002/nbm.4236:e4236

  35. Dhamala E, Abdelkefi I, Nguyen M, Hennessy TJ, Nadeau H, Near J (2019) Validation of in vivo MRS measures of metabolite concentrations in the human brain. NMR Biomed 32(3):e4058

    Article  PubMed  Google Scholar 

  36. Bednarik P, Moheet A, Deelchand DK, Emir UE, Eberly LE, Bares M, Seaquist ER, Oz G (2015) Feasibility and reproducibility of neurochemical profile quantification in the human hippocampus at 3 T. NMR Biomed 28(6):685–693

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Chiu PW, Mak HK, Yau KK, Chan Q, Chang RC, Chu LW (2014) Metabolic changes in the anterior and posterior cingulate cortices of the normal aging brain: proton magnetic resonance spectroscopy study at 3 T. Age (Dordr) 36(1):251–264

    Article  CAS  PubMed  Google Scholar 

  38. Lee HH, Kim H (2022) Bayesian deep learning-based (1) H-MRS of the brain: metabolite quantification with uncertainty estimation using Monte Carlo dropout. Magn Reson Med 88(1):38–52

    Article  PubMed  Google Scholar 

  39. Dziadosz M, Rizzo R, Kyathanahally SP, Kreis R (2023) Denoising single MR spectra by deep learning: miracle or mirage? Magn Reson Med. https://doi.org/10.1002/mrm.29762

    Article  PubMed  Google Scholar 

  40. Giapitzakis IA, Borbath T, Murali-Manohar S, Avdievich N, Henning A (2019) Investigation of the influence of macromolecules and spline baseline in the fitting model of human brain spectra at 9.4T. Magn Reson Med 81(2):746–758

    Article  CAS  PubMed  Google Scholar 

  41. Marjanska M, Terpstra M (2021) Influence of fitting approaches in LCModel on MRS quantification focusing on age-specific macromolecules and the spline baseline. NMR Biomed 34(5):e4197

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors thank the Taiwan Mind & Brain Imaging Center (TMBIC) and National Chengchi University for the instrument availability. The TMBIC is supported by the National Science and Technology Council, Taiwan. This work was supported in part by grants from the National Science and Technology Council, Taiwan (108-2314-B-004-001, 111-2314-B-004 -001, 107-2221-E-011-053)

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Authors and Affiliations

  1. Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Yu-Long Huang & Yi-Ru Lin

  2. Graduate Institute of Applied Physics, National Chengchi University, No.64, Sec.2, ZhiNan Rd., Wenshan District, Taipei, 11605, Taiwan

    Shang-Yueh Tsai

  3. Research Center for Mind, Brain and Learning, National Chengchi University, Taipei, Taiwan

    Shang-Yueh Tsai

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  1. Yu-Long Huang
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Contributions

S.-Y. T. conceived and designed the experiments. Y.-L. H. and S.-Y. T. performed the experiments. Y.-L. H. and Y.-R. L. analyzed the data. S.-Y. T, Y.-L. H., and Y.-R. L interpreted the data. S.-Y. T. and Y.-L. H. wrote the main manuscript text and prepared the tables and figures. S.-Y. T. and Y.-R. L revised the manuscript critically for important intellectual content. All authors reviewed the manuscript and approved the final version.

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Correspondence to Shang-Yueh Tsai.

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Huang, YL., Lin, YR. & Tsai, SY. Comparison of convolutional-neural-networks-based method and LCModel on the quantification of in vivo magnetic resonance spectroscopy. Magn Reson Mater Phy (2023). https://doi.org/10.1007/s10334-023-01120-z

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  • DOI: https://doi.org/10.1007/s10334-023-01120-z

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