Rathi, YogeshNing, LipengMichailovich, OlegLiao, HuijunGagoski, BorjanGrant, P.Shenton, MarthaStern, RobertWestin, Carl-FredrikLin, Alexander2016-09-202014Rathi, Yogesh, Lipeng Ning, Oleg Michailovich, HuiJun Liao, Borjan Gagoski, P. Ellen Grant, Martha E. Shenton, Robert Stern, Carl-Fredrik Westin, and Alexander Lin. 2014. “Maximum Entropy Estimation of Glutamate and Glutamine in MR Spectroscopic Imaging.” Lecture Notes in Computer Science: 749–756. doi:10.1007/978-3-319-10470-6_93.0302-9743http://nrs.harvard.edu/urn-3:HUL.InstRepos:28539562Magnetic resonance spectroscopic imaging (MRSI) is often used to estimate the concentration of several brain metabolites. Abnormalities in these concentrations can indicate specific pathology, which can be quite useful in understanding the disease mechanism underlying those changes. Due to higher concentration, metabolites such as N-acetylaspartate (NAA), Creatine (Cr) and Choline (Cho) can be readily estimated using standard Fourier transform techniques. However, metabolites such as Glutamate (Glu) and Glutamine (Gln) occur in significantly lower concentrations and their resonance peaks are very close to each other making it di!cult to accurately estimate their concentrations (separately). In this work, we propose to use the theory of ‘Spectral Zooming’ or high-resolution spectral analysis to separate the Glutamate and Glutamine peaks and accurately estimate their concentrations. The method works by estimating a unique power spectral density, which corresponds to the maximum entropy solution of a zero-mean stationary Gaussian process. We demonstrate our estimation technique on several physical phantom data sets as well as on invivo brain spectroscopic imaging data. The proposed technique is quite general and can be used to estimate the concentration of any other metabolite of interest.en-USJournal Article2016-09-2010.1007/978-3-319-10470-6_93