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Journal Article

Using spatial prior knowledge in the spectral fitting of MRS images

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84402

Kaster FO, Henning,  A
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Kelm, B., Kaster FO, Henning, A., Weber M-A, Bachert P, Boesiger P, Hamprecht, F., & Menze, B. (2012). Using spatial prior knowledge in the spectral fitting of MRS images. NMR in Biomedicine, 25(1), 1–13. doi:10.1002/nbm.1704.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-B86E-1
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.