02062nas a2200217 4500008004100000245007400041210006900115260001200184300000700196490000700203520142100210653001501631653002501646653002501671100002401696700001201720700001801732700001501750700001201765856006701777 2009 eng d00aIn Vivo Quantitation of Metabolites with an incomplete Model Function0 aIn Vivo Quantitation of Metabolites with an incomplete Model Fun c10/2009 a100 v203 aMetabolites can serve as biomarkers. Estimation of metabolite
concentrations from an in vivo magnetic resonance spectroscopy (MRS)
signal often uses a reference signal to estimate a model function of the
spectral lineshape. When no reference signal is available, the a priori
unknown in vivo lineshape must be inferred from the data at hand. This
makes quantitation of metabolites from in vivo MRS signals a
semi-parametric estimation problem which, in turn, implies setting of
hyper-parameters by users of the software involved. Estimation of
metabolite concentrations is usually done by nonlinear least-squares
(NLLS) fitting of a physical model function based on minimizing the
residue. In this work, the semi-parametric task is handled by
complementing the usual criterion of minimal residue with a second
criterion acting in tandem with it. This second criterion is derived
from the general physical knowledge that the width of the line is
limited. The limit on the width is a hyper-parameter; its setting
appeared not critical so far. The only other hyper-parameter is the
relative weight of the two criteria. But its setting too is not
critical. Attendant estimation errors, obtained from a Monte Carlo
calculation, show that the two-criterion NLLS approach successfully
handles the semi-parametric aspect of metabolite quantitation.10acateg_st2i10aImagerie cérébrale10areseau_international1 aGraveron-Demilly, D1 aPopa, E1 aCapobianco, E1 aOrmondt, D1 aBeer, R uhttps://www.creatis.insa-lyon.fr/site7/fr/publications/GRAV-09