Abstract
MRSI can reflect the abnormal metabolites information of different diseases in clinical diagnosis. We made research on the application of SVD-based metabolite quantification methods in 2D MRSI by comparing two different SVD algorithms. In the quantification process, first, the FID signals are rearranged into a data matrix. Then, we can make full SVD by Golub algorithm or partial SVD by Lanczos algorithm. Last, the parameter estimation on each metabolite can be acquired by the definition of the linear parameter model. The ordinary full SVD must decompose all the singular value, with a big cost of the time. The partial SVD just needs to calculate the less singular by the character of the Hankel matrix to improve the estimation speed. When the SNR of MRS signals is higher than ten, the computation time on partial SVD is decreased by thirteen times of the ordinary method. But the speed of quantification is only half of the ordinary one when the SNR is lower than one. Improvements of speed and accuracy in metabolite quantification are key factors for 2D MRSI to be a clinical tool in the future.
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References
Gruber, S., Stadlbauer, A., Mlynarik, V.: Proton magnetic resonance spectroscopic imaging in brain tumor diagnosis. Neurosurg Clin. N Am. 16, 101–114 (2005)
Vermathen, P., Laxer, K., Schuff, N., Matson, G.: Evidence of neuronal injury outside the medial temporal lobe in temporal lobe epilepsy: N-acetylaspartate concentration reductions detected with multisection proton MR spectroscopic imaging. Radiology 226, 195–202 (2003)
Golub, G., Pereyra, V.: The differentiation of pseudo-inverses and nonlinear least squares problems whose variables separate. SIAM J. Numer. Anal. 10, 413–432 (1973)
Brown, T., Kincaid, B., Ugurbil, K.: NMR chemical shift imaging in three dimensions. Proceedings National Academy of Sciences 79, 3523–3526 (1982)
Vanhamme, L., Sundin, T., Hecke, P.: MR spectroscopy quantitation: a reviews of time-domain methods. NMR in Biomedcine 14, 233–246 (2001)
Golub, G., Reinsch, C.: Singular value decomposition and least squares solutions. Numer. Math. 14, 403–420 (1970)
Laudadio, T., Mastronardi, N., Vanhamme, L., Hecke, P., Huffel, S.: Improved Lanczos Algorithms for Blackbox MRS Data Quantitation. Journal of Magnetic Resonance 157, 292–297 (2002)
Ricardo, D., Eric, P.: Lanczos and the Riemannian SVD in information retrieval applications. Numerical Linear Algebra with applications 3, 1–18 (2003)
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© 2006 Springer-Verlag Berlin Heidelberg
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Huang, M., Lu, S. (2006). Application of SVD-Based Metabolite Quantification Methods in Magnetic Resonance Spectroscopic Imaging. In: Yang, GZ., Jiang, T., Shen, D., Gu, L., Yang, J. (eds) Medical Imaging and Augmented Reality. MIAR 2006. Lecture Notes in Computer Science, vol 4091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11812715_16
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DOI: https://doi.org/10.1007/11812715_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37220-2
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