Using spatial prior knowledge in the spectral fitting of MRS images

Abstract : We propose a Bayesian smoothness prior in the spectral fitting of MRS images which can be used in addition to commonly employed prior knowledge. By combining a frequency-domain model for the free induction decay with a Gaussian Markov random field prior, a new optimization objective is derived that encourages smooth parameter maps. Using a particular parameterization of the prior, smooth damping, frequency and phase maps can be obtained whilst preserving sharp spatial features in the amplitude map. A Monte Carlo study based on two sets of simulated data demonstrates that the variance of the estimated parameter maps can be reduced considerably, even below the Cramér-Rao lower bound, when using spatial prior knowledge. Long-TE 1H MRSI at 1.5 T of a patient with a brain tumor shows that the use of the spatial prior resolves the overlapping peaks of choline and creatine when a single voxel method fails to do so. Improved and detailed metabolic maps can be derived from high-spatial-resolution, short-TE 1H MRSI at 3 T. Finally, the evaluation of four series of long-TE brain MRSI data with various signal-to-noise ratios shows the general benefit of the proposed approach.
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Soumis le : vendredi 19 août 2011 - 19:57:02
Dernière modification le : mardi 5 mars 2019 - 09:30:10

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B. Michael Kelm, Frederik O. Kaster, A. Henning, Marc-André Weber, P. Bachert, et al.. Using spatial prior knowledge in the spectral fitting of MRS images. NMR in Biomedicine, Wiley, 2012, 25 (1), pp.1-13. 〈10.1002/nbm.1704〉. 〈inria-00616193〉



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