MULTIVOXEL MR SPECTROSCOPY TOOL FOR BRAIN CANCER DETECTION IN NEURONAVIGATION - Performance

Juan José Fuertes, Valery Naranjo, Pablo González, Ángela Bernabeu, Mariano Alcañiz, Javier Sanchez

2012

Abstract

This work presents a simple and interactive spectroscopic tool to help clinicians for brain cancer detection. Firstly, Magnetic Resonance Spectroscopy (MRS) and Magnetic Resonance Imaging (MRI) are registered to perform brain analysis. After processing the spectroscopic signals with HLSVD method for water suppression, zero-filling and phase-correction algorithms, and apodization functions to improve the signal-to-noise ratio (SNR), the metabolite brain maps are generated in order to analyze brain composition. A 3D-spatial distribution of the anatomical and spectroscopic images and how they are registered are presented to facilitate surgery planning. The goal is to generate metabolite brain maps which can be merged with anatomical images in the neuronavigator to provide the surgeon with the exact point where performing the biopsy.

References

  1. Cabanes, E., Confort-Gouny, S., Le Fur, Y., Simond, G. and Cozzone, P.J. (2001). Optimization of Residual Water Signal Removal by HLSVD on Simulated Short Echo Time Proton MR Spectra of the Human Brain. Journal of Magnetic Resonance. 150:116-125.
  2. Castillo, M., Kwock, L., and Mukherji, S. (1996). Clinical application of proton MR spectroscopy. American Journal of Neuroradiology. 17:1-15.
  3. De Beer, R. and Van Ormondt, D. (1992). Analysis of NMR data using time domain fitting procedures. NMR Basic Principles and Progress. 26:202-48.
  4. Gruber, S., Stadibauer, A., Mlynarik, V., Ganslandt, O., and Moser, E. (2004). An LCModel-based automatic software tool for the reconstruction of anatomicallyand pathologically-matched voxels using highresolution 3D-spectroscopic imaging: Applications in tumour patients. In Proc. ISMRM, volume 11.
  5. Laudadio, T., Mastronardi, N., Vanhamme, L., Van Hecke, P. and Van Huffel, S. (2002). Improved Lanczos algorithms for blackbox MRS data quantitation. Journal of Magnetic Resonance. 157:292-297.
  6. Pijnappel, W. W. F., Van den Boogaart, A., De Beer, R., and Van Ormondt, D. (1992). SVD-based quantiffication of magnetic resonance signals. Journal of Magnetic Resonance. 97(1):122-134.
  7. Poullet, J., Sima, D. M., Van Huffel, S., and Van Hecke, P. (2007). Frequency-selective quantitation of shortecho time 1H magnetic resonance spectra. Journal of Magnetic Resonance. 186(2):293-304,
  8. Ratiney, H., Sdika, M., Coenradie, Y., Cavassila, S., Van Ormondt, D., and Graveron-Demilly, D. (2005). Time domain semi-parametric estimation based on a metabolite basis set. NMR in Biomedicine. 18(1):1-13.
  9. Simonetti, A. W., Poullet, J-B., Sima, D. M., De Neuter, B., Vanhamme, L., Lemmerling, P., and Van Huffel S. (2006). An open source short echo time MR quantitation software solution: AQSES.
  10. 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, volume 20.
  11. Stoyanova, R., Kuesel, A., and Brown, T. (1995) Application of principal-component analysis for spectral Quantitation. Journal of Magnetic Resonance. 115:265-269.
  12. Van Huffel, S., Chen, H., Decanniere, C., and Van Hecke, P. (1994) Algorithm for time-domain NMR data fitting based on total least squares. Journal of Magnetic Resonance. 110:228-237.
  13. Vanhamme, L., and Van Huffel, S. (1997). Improved method for accurate and efficient quantification of MRS data with use of prior-knowledge. Journal of Magnetic Resonance. 129(1):35-43.
  14. Vanhamme, L., Sundin, T., Hecke, P. V., and Huffel, S. V. (2001). MR Spectroscopic Quantitation: a Review of Time-Domain Methods. NMR in Biomedicine. 14:233- 246.
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Paper Citation


in Harvard Style

Fuertes J., Naranjo V., González P., Bernabeu Á., Alcañiz M. and Sanchez J. (2012). MULTIVOXEL MR SPECTROSCOPY TOOL FOR BRAIN CANCER DETECTION IN NEURONAVIGATION - Performance . In Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2012) ISBN 978-989-8425-91-1, pages 167-172. DOI: 10.5220/0003770501670172


in Bibtex Style

@conference{biodevices12,
author={Juan José Fuertes and Valery Naranjo and Pablo González and Ángela Bernabeu and Mariano Alcañiz and Javier Sanchez},
title={MULTIVOXEL MR SPECTROSCOPY TOOL FOR BRAIN CANCER DETECTION IN NEURONAVIGATION - Performance},
booktitle={Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2012)},
year={2012},
pages={167-172},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003770501670172},
isbn={978-989-8425-91-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2012)
TI - MULTIVOXEL MR SPECTROSCOPY TOOL FOR BRAIN CANCER DETECTION IN NEURONAVIGATION - Performance
SN - 978-989-8425-91-1
AU - Fuertes J.
AU - Naranjo V.
AU - González P.
AU - Bernabeu Á.
AU - Alcañiz M.
AU - Sanchez J.
PY - 2012
SP - 167
EP - 172
DO - 10.5220/0003770501670172