Back to overview

Fitting interrelated datasets: metabolite diffusion and general lineshapes

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Adalid V., Döring A., Kyathanahally S. P., Bolliger C. S., Boesch C., Kreis R.,
Project Magnetic resonance techniques to determine metabolite levels: extending scope and clinical robustness
Show all

Original article (peer-reviewed)

Journal MAGMA
Volume (Issue) 30
Page(s) 429 - 448
Title of proceedings MAGMA
DOI 10.1007/s10334-017-0618-z

Open Access

Type of Open Access Publisher (Gold Open Access)


OBJECTIVE: Simultaneous modeling of true 2-D spectroscopy data, or more generally, interrelated spectral datasets has been described previously and is useful for quantitative magnetic resonance spectroscopy applications. In this study, a combined method of reference-lineshape enhanced model fitting and two-dimensional prior-knowledge fitting for the case of diffusion weighted MR spectroscopy is presented. MATERIALS AND METHODS: Time-dependent field distortions determined from a water reference are applied to the spectral bases used in linear-combination modeling of interrelated spectra. This was implemented together with a simultaneous spectral and diffusion model fitting in the previously described Fitting Tool for Arrays of Interrelated Datasets (FiTAID), where prior knowledge conditions and restraints can be enforced in two dimensions. RESULTS: The benefit in terms of increased accuracy and precision of parameters is illustrated with examples from Monte Carlo simulations, in vitro and in vivo human brain scans for one- and two-dimensional datasets from 2-D separation, inversion recovery and diffusion-weighted spectroscopy (DWS). For DWS, it was found that acquisitions could be substantially shortened. CONCLUSION: It is shown that inclusion of a measured lineshape into modeling of interrelated MR spectra is beneficial and can be combined also with simultaneous spectral and diffusion modeling.