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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| Hauptverfasser: | , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2012
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| In: |
NMR in biomedicine
Year: 2012, Jahrgang: 25, Heft: 1, Pages: 1-13 |
| ISSN: | 1099-1492 |
| DOI: | 10.1002/nbm.1704 |
| Online-Zugang: | 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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| Verfasserangaben: | B. Michael Kelm, Frederik O. Kaster, Anke Henning, Marc-André Weber, Peter Bachert, Peter Boesiger, Fred A. Hamprecht, Bjoern H. Menze |
MARC
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| 245 | 1 | 0 | |a Using spatial prior knowledge in the spectral fitting of MRS images |c B. Michael Kelm, Frederik O. Kaster, Anke Henning, Marc-André Weber, Peter Bachert, Peter Boesiger, Fred A. Hamprecht, Bjoern H. Menze |
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| 520 | |a 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. | ||
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| 650 | 4 | |a quantification | |
| 650 | 4 | |a spatial prior knowledge | |
| 650 | 4 | |a spectral fitting | |
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