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Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

D. Das, E. Coello, R. Schulte, B. Menze
Spectral Denoising for MR Spectroscopic Imaging using Non-Local Means
Proceedings of the ISMRM Workshop on MR Spectroscopy, Lake Constance, Germany, August 2016 (bib)

Magnetic resonance spectroscopic imaging (MRSI) is an imaging modality used for studying tissues in-vivo in order to assess and quantify metabolites for diagnostic purposes. However, long scanning times, low spatial resolution, poor signal-to-noise ratio (SNR) and the subsequent noise-sensitive non-linear model fitting are major roadblocks in accurately quantifying the metabolite concentration. In this work, we propose a spectrally adaptive method for denoising of MRSI spectra based on the concept of Non-Local Means (NLM)1,2 which relies on spectral redundancy in the given data-set. Due to lack of phase information in the acquired data, we perform a ‘spectral dephasing’ step to consider a wide range of phase variations to improve the NLM-based pattern analysis. We evaluated our method on human in-vivo data and assessed the improvement in SNR for NAA.
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