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Using spatial prior knowledge in the spectral fitting of MRS images

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...

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Bibliographic Details
Main Authors: Kelm, Bernd Michael (Author) , Kaster, Frederik Orlando (Author) , Weber, Marc-André (Author) , Bachert, Peter (Author) , Hamprecht, Fred (Author)
Format: Article (Journal)
Language:English
Published: 2012
In: NMR in biomedicine
Year: 2012, Volume: 25, Issue: 1, Pages: 1-13
ISSN:1099-1492
DOI:10.1002/nbm.1704
Online Access:Verlag, Volltext: http://dx.doi.org/10.1002/nbm.1704
Verlag, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/nbm.1704
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Author Notes:B. Michael Kelm, Frederik O. Kaster, Anke Henning, Marc-André Weber, Peter Bachert, Peter Boesiger, Fred A. Hamprecht, Bjoern H. Menze
Description
Summary: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. Copyright © 2011 John Wiley & Sons, Ltd.
Item Description:First published: 28 April 2011
Gesehen am 02.08.2018
Physical Description:Online Resource
ISSN:1099-1492
DOI:10.1002/nbm.1704

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