A statistical framework for in vivo spectroscopic imaging

Abstract Two of the major difficulties limiting in vivo spectroscopic imaging are poor SNR and main main field inhomogeneities. On the basis that the data have been collected by an imaging method which results in a localized time signal from each voxel of interest, the analysis of the data is formulated as a parametric estimation problem. By incorporating the available a priori information into this statistical framework, optimum estimates are computed. Computation time and numerical accuracy are also considered. The resulting estimates are then presented in an image format, so that the information is readily correlated with any known anatomical or physiological patterns. In addition, we describe a fast, robust algorithm, based on envelope detection, for imaging metabolites in the 1H spectrum. Simulations as well as experimental results are presented.

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