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NMR Biomed. 2012 Jan;25(1):1-13. doi: 10.1002/nbm.1704. Epub 2011 Apr 28.

Using spatial prior knowledge in the spectral fitting of MRS images.

Author information

1
Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Germany. michael.kelm@siemens.com

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 (1)H 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 (1)H 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.

PMID:
21538636
DOI:
10.1002/nbm.1704
[Indexed for MEDLINE]

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