title: Using spatial prior knowledge in the spectral fitting of MRS images.
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.
authors.affiliation: Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Germany. michael.kelm@siemens.com
authors.forename: B Michael
authors.initials: BM
authors.lastname: Kelm
authors.forename: Frederik O
authors.initials: FO
authors.lastname: Kaster
authors.forename: Anke
authors.initials: A
authors.lastname: Henning
authors.forename: Marc-André
authors.initials: MA
authors.lastname: Weber
authors.forename: Peter
authors.initials: P
authors.lastname: Bachert
authors.forename: Peter
authors.initials: P
authors.lastname: Boesiger
authors.forename: Fred A
authors.initials: FA
authors.lastname: Hamprecht
authors.forename: Bjoern H
authors.initials: BH
authors.lastname: Menze
mesh_terms: D001932:Brain Neoplasms / Q000473:pathology; D003198:Computer Simulation; D019992:Databases as Topic; D006801:Humans; D019359:Knowledge; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008954:Models, Biological