Keywords: Data Processing, Spectroscopy, Singular value decomposition, MR spectroscopic imaging, Denoising
The utility of MR spectroscopic imaging (MRSI) can be limited by a low signal-to-noise ratio (SNR) in practice. Averaging multiple coherent repetitions increases the SNR, but at the cost of time-consuming acquisition. Several computationally expensive approaches based on low-rank matrix approximation for denoising MRSI data have been proposed, which do not take advantage of previously acquired spectra.
This work demonstrates a novel computationally cheap data-driven approach to MRSI denoising, coined EigenMRS, by learning low-rank structures of MRS data. As proof of concept, EigenMRS was tested against the simulated 1H- MRSI data, and the results showed an increase in denoising performance.
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