Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-56470
Kelm, B M; Kaster, F O; Henning, A; Weber, M A; Bachert, P; Boesiger, P; Hamprecht, F A; Menze, B H (2012). Using spatial prior knowledge in the spectral fitting of MRS images. NMR in Biomedicine, 25(1):1-13.
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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. Copyright © 2011 John Wiley & Sons, Ltd.
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|Item Type:||Journal Article, refereed, original work|
|Communities & Collections:||04 Faculty of Medicine > Institute of Biomedical Engineering|
|Dewey Decimal Classification:||170 Ethics
610 Medicine & health
|Deposited On:||02 Apr 2012 06:46|
|Last Modified:||05 Apr 2016 15:27|
|Free access at:||Publisher DOI. An embargo period may apply.|
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