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Automated spectral analysis III: Application to in Vivo proton MR Spectroscopy and spectroscopic imaging
Author(s) -
Soher Brian J.,
Young Karl,
Govindaraju Varanavasi,
Maudsley Andrew A.
Publication year - 1998
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.1910400607
Subject(s) - magnetic resonance spectroscopic imaging , a priori and a posteriori , nuclear magnetic resonance , artificial intelligence , nonparametric statistics , proton magnetic resonance , pattern recognition (psychology) , in vivo , computer science , proton , signal (programming language) , data acquisition , magnetic resonance imaging , parametric statistics , metabolite , biological system , chemistry , physics , mathematics , radiology , statistics , medicine , philosophy , microbiology and biotechnology , epistemology , quantum mechanics , biology , programming language , operating system , biochemistry
An automated method for analysis of in vivo proton magnetic resonance (MR) spectra and reconstruction of metabolite distributions from MR spectroscopic imaging (MRSI) data is described. A parametric spectral model using acquisition specific, a priori information is combined with a wavelet‐based, nonparametric characterization of baseline signals. For image reconstruction, the initial fit estimates were additionally modified according to a priori spatial constraints. The automated fitting procedure was applied to four different examples of MRS data obtained at 1.5 T and 4.1 T. For analysis of major metabolites at medium TE values, the method was shown to perform reliably even in the presence of large baseline signals and relatively poor signal‐to‐noise ratios typical of in vivo proton MRSI. identification of additional metabolites was also demonstrated for short TE data. Automated formation of metabolite images will greatly facilitate and expand the clinical applications of MR spectroscopic imaging.