Adequate suppression of water signal is vital in detecting low concentration metabolites in in-vivo proton spectroscopy acquisitions. This problem, which is exacerbated in the presence of multiplicative noise induced by physiological motion, is not addressed by current water and lipid saturation-based approaches. Here we use a time series modeling approach and show that signal estimation techniques are extremely effective in suppressing the highly correlated physiological noise component to achieve over three orders of magnitude in-vivo water suppression.
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