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Using spatial prior knowledge in the spectral fitting of magnetic resonance spectroscopic images

B Kelm and F Kaster and A Henning and M Weber and F Hamprecht and B Menze
NMR in Biomedicine
Vol. 25, pp. 1-13, 2012

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.stic impact of MRSI of the human prostate.


Link to publisher's page
@Article{eth_biwi_00983,
  author = {B Kelm and F Kaster and A Henning and M Weber and F Hamprecht and B Menze},
  title = {Using spatial prior knowledge in the spectral fitting of magnetic resonance spectroscopic images },
  journal = {NMR in Biomedicine},
  year = {2012},
  month = {},
  pages = {1-13},
  volume = {25},
  number = {},
  keywords = {}
}