Quantification and classification of high-resolution magic angle spinning data for brain tumor diagnosis

Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

Volumn , Issue , 2007, Pages 5407-5410

  Poullet, Jean Baptiste ^a   Martinez Bisbal, M Carmen ^b   Valverde, Dani ^c   Monleon, Daniel ^d   Celda, Bernardo ^b   Arús, Carles ^c   Van Huffel, Sabine ^a  

^a Katholieke Universiteit Leuven   (Belgium)

^b INSTITUTO DE SALUD CARLOS III   (Spain)

^c UNIVERSITAT AUTÒNOMA DE BARCELONA   (Spain)

^d UNIVERSITY OF VALENCIA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN; FEATURE EXTRACTION; LEAST SQUARES APPROXIMATIONS; PROTONS; SUPPORT VECTOR MACHINES; TUMORS;

ANGLE SPINNING; HIGH-RESOLUTION MAGIC-ANGLE SPINNING; RANDOM SAMPLING;

ONCOLOGY;

DIAGNOSTIC AGENT; PROTON; TUMOR MARKER;

ALGORITHM; ARTICLE; BRAIN TUMOR; COMPUTER ASSISTED DIAGNOSIS; EVALUATION STUDY; HUMAN; METABOLISM; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SPIN LABELING;

ALGORITHMS; BRAIN NEOPLASMS; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; MAGNETIC RESONANCE SPECTROSCOPY; PROTONS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SPIN LABELS; TUMOR MARKERS, BIOLOGICAL;

EID: 57649243104     PISSN: 05891019     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2007.4353565     Document Type: Conference Paper

References (13)

1. L. L. Cheng, D. C. Anthony, A. R. Comite, P. M. Black, A. A. Tzika, and R. G. Gonzalez, "Quantification of microheterogeneity in glioblastoma multiforme with ex vivo high-resolution magic-angle spinning (HRMAS) proton magnetic resonance spectroscopy," Neumoncol, vol. 2, no. 2, pp. 87-95, 2000.
2. 1H MR spectra," Int. J. Mol. Med., vol. 18, no. 5, pp. 859-69, 2006.
3. 1H MRS study of human high grade gliomas," NMR Biomed., vol. 17, pp. 191-205, 2004.
4. T. Laudadio, N. Mastronardi, L. Vanhamme, P. Van Hecke, and S. Van Huffel, "Improved Lanczos algorithms for blackbox MRS data quantitation," J. Magn. Reson., vol. 157, no. 2, pp. 292-7, 2002.
5. J. B. Poullet, D. M. Sima, A. W. Simonetti, B. De Neuter, L. Vanhamme, P. Lemmerling, and S. Van Huffel, "An automated quantitation of short echo time MRS spectra in an open source software environment: AQSES," NMR Biomed., 2006.
6. M. Levitt, Spin Dynamics: Basics of Nuclear Magnetic Resonance. Wiley, 2001.
7. J. A. K. Suykens and J. Vandewalle, "Least squares support vector machine classifiers," Neural Process. Lett., vol. 9, no. 3, pp. 293-300, 1999.
8. 1H MR spectra," J. Magn. Reson., vol. 170, no. 1, pp. 164-175, 2004.
9. L. Lukas, A. Devos, J. A. K. Suykens, L. Vanhamme, F. A. Howe, C. Majos, A. Moreno Torres, C. Van der Graaf, A. R. Tate, C. Arus, and S. Van Huffel, "Brain tumor classification based on long echo proton MRS signals," Artif. Intell. Med., vol. 31, pp. 73-89, 2004.
10. 1H brain tumor spectra: a multicenter study," Magn. Reson. Med., vol. 49, no. 1, pp. 29-36, 2003.
11. B. H. Menze, M. P. Lichy, P. Bachert, B. M. Keim, H. P. Schlemmer, and F. A. Hamprecht, "Optimal classification of long echo time in vivo magnetic resonance spectra in the detection of recurrent brain tumors," NMR Biomed., vol. 19, no. 5, pp. 599-609, 2006.
12. C. Majos and M. Julia Sape, "Brain tumor classification by proton MR spectroscopy: Comparison of diagnostic accuracy at short and long TE," Am. J. Neuradiol., vol. 25, pp. 1696-1704, 2004.
13. A. W. Simonetti, W. J. Melssen, F. Szabo de Edelenyi, J. J. van Asten, A. Heerschap, and L. M. Buydens, "Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification," NMR Biomed., vol. 18, no. 1, pp. 34-43, 2005.