DAMPING FACTOR CONSTRAINTS AND METABOLITE PROFILE SELECTION INFLUENCE MAGNETIC RESONANCE SPECTROSCOPY DATA QUANTIFICATION

M. I. Osorio Garcia, D. M. Sima, S. Van Huffel, F. U. Nielsen, U. Himmelreich

2011

Abstract

Magnetic Resonance Spectroscopy (MRS) is a technique used for the diagnostics of tumour and metabolic diseases by estimating the metabolite concentrations of the tissue under investigation. Unreliable metabolite estimation may mislead the diagnosis and therefore quantification of MRS in vivo signals must be performed carefully. In this work, we quantify 1.5 Tesla (T) and 9.4 T MRS in vivo signals and study the influence of the damping factor constraint and the metabolite profile selection used in the quantification method. The damping factor bounds the linewidth of the metabolite profiles and may yield bad fits if wrongly selected. Furthermore, MRS data quantification leads to overestimation of some metabolite concentrations when the selected metabolite basis set is incomplete suggesting that metabolites are fitting the region of their neighboring components. Here, we evaluate the normality of the residual which in cases of good fitting contains no metabolites and only white Gaussian noise. Furthermore, we propose to estimate the damping bound adaptively by taking into account information from the linewidth of the signal and the metabolite basis set.

References

  1. Bottomley, P. (1984). Selective volume method for performing localized NMR spectroscopy. in U.S patent, (4 480 228).
  2. Cudalbu, C., Cavassila, S., Ratiney, H., Grenier, D., Briguet, A., and Graveron-Demilly, D. (2006). Estimation of metabolite concentrations of healthy mouse brain by magnetic resonance spectroscopy at 7T. Comptes Rendus Chimie, 9(3-4):534 - 538.
  3. Grage, H. and Akke, M. (2003). A statistical analysis of NMR spectrometer noise. Journal of Magnetic Resonance, 162(1):176 - 188.
  4. Gruetter, R. (1993). Automatic localized in vivo adjustment of all first- and second- order shim coils. Magnetic Resonance in Medicine, 29(6):804-811.
  5. Klose, U. (1990). In vivo proton spectroscopy in presence of eddy currents. Magnetic Resonance in Medicine, 14(1):26 - 30.
  6. Laudadio, T., Mastronardi, N., Vanhamme, L., Van Hecke, P., and Van Huffel, S. (2002). Improved Lanczos algorithms for blackbox MRS data quantitation. Jorunal of Magnetic Resonance, 157(2):292 - 297.
  7. Pfeuffer, J., Tkc, I., Provencher, S. W., and Gruetter, R. (1999). Toward an in vivo neurochemical profile: Quantification of 18 metabolites in short-echo-time 1H NMR spectra of the rat brain. Journal of Magnetic Resonance, 141(1):104 - 120.
  8. Pijnappel, W. W. F., van den Boogaart, A., de Beer, R., and van Ormondt, D. (1992). SVD-based quantification of magnetic resonance signals. Journal of Magnetic Resonance, 97(1):122 - 134.
  9. Poullet, J.-B., Sima, D., Simonetti, A., De Neuter, B., Vanhamme, L., Lemmerling, P., and Van Huffel, S. (2007). An automated quantitation of short echo time MRS spectra in an open source software environment: AQSES. NMR in Biomedicine, 20(5):493 - 504.
  10. Provencher, S. (2001). Automatic quantitation of localized in vivo 1H spectra with LCModel. NMR in Biomedicine, 14(4):260-264.
  11. Ratiney, H., Coenradie, Y., Cavassila, S., van Ormondt, D., and Graveron-Demilly, D. (2004). Time-domain quantitation of 1H short echo-time signals: background accommodation. MAGMA, 16(6):284 - 296.
  12. Slotboom, J., Nirkko, A., Brekenfeld, C., and van Ordmont, D. (2009). Reliability testing of in vivo magnetic resonance spectroscopy (MRS) signals and signal artifact reduction by order statistics filtering. Measurement Science and Technology, 20(104030):14pp.
  13. Stefan, D., Di Cesare, F., Andrasescu, A., Popa, E., Lazariev, A., Vescovo, E., Strbak, O., Williams, S., Starcuk, Z., Cabanas, M., van Ormondt, D., and Graveron-Demilly., D. (2009). Quantitation of magnetic resonance spectroscopy signals: the jMRUI software package. Measurement Science and Technology, 20(10):104035(9pp).
  14. Tkác?, I., Starc?uk, Z., Choi, I.-Y., and Gruetter, R. (1999). In vivo 1H NMR spectroscopy of rat brain at 1ms echo time. Magnetic Resonance in Medicine, 41(4):649- 656.
Download


Paper Citation


in Harvard Style

I. Osorio Garcia M., M. Sima D., Van Huffel S., U. Nielsen F. and Himmelreich U. (2011). DAMPING FACTOR CONSTRAINTS AND METABOLITE PROFILE SELECTION INFLUENCE MAGNETIC RESONANCE SPECTROSCOPY DATA QUANTIFICATION . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011) ISBN 978-989-8425-35-5, pages 176-181. DOI: 10.5220/0003139301760181


in Bibtex Style

@conference{biosignals11,
author={M. I. Osorio Garcia and D. M. Sima and S. Van Huffel and F. U. Nielsen and U. Himmelreich},
title={DAMPING FACTOR CONSTRAINTS AND METABOLITE PROFILE SELECTION INFLUENCE MAGNETIC RESONANCE SPECTROSCOPY DATA QUANTIFICATION},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)},
year={2011},
pages={176-181},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003139301760181},
isbn={978-989-8425-35-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)
TI - DAMPING FACTOR CONSTRAINTS AND METABOLITE PROFILE SELECTION INFLUENCE MAGNETIC RESONANCE SPECTROSCOPY DATA QUANTIFICATION
SN - 978-989-8425-35-5
AU - I. Osorio Garcia M.
AU - M. Sima D.
AU - Van Huffel S.
AU - U. Nielsen F.
AU - Himmelreich U.
PY - 2011
SP - 176
EP - 181
DO - 10.5220/0003139301760181