Parameterization of metabolite and macromolecule contributions in interrelated MR spectra of human brain using multidimensional modeling.

Hoefemann, Maike; Bolliger, Christine Sandra; Chong, Daniel G.Q.; van der Veen, Jan Willem; Kreis, Roland (2020). Parameterization of metabolite and macromolecule contributions in interrelated MR spectra of human brain using multidimensional modeling. (In Press). NMR in biomedicine(e4328), e4328. Wiley 10.1002/nbm.4328

[img] Text
Parameterization.pdf - Published Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (3MB) | Request a copy
[img] Text
Hoefemann_etal__lastsubmittedVs.pdf - Accepted Version
Restricted to registered users only until 16 June 2021.
Available under License Publisher holds Copyright.

Download (5MB) | Request a copy

Macromolecular signals are crucial constituents of short echo-time 1 H MR spectra with potential clinical implications in themselves as well as essential ramifications for the quantification of the usually targeted metabolites. Their parameterization, needed for general fitting models, is difficult because of their unknown composition. Here, a macromolecular signal parameterization together with metabolite signal quantification including relaxation properties is investigated by multidimensional modeling of interrelated 2DJ inversion-recovery (2DJ-IR) datasets. Simultaneous and iterative procedures for defining the macromolecular background (MMBG) as mono-exponentially or generally decaying signals over TE are evaluated. Varying prior knowledge and restrictions in the metabolite evaluation are tested to examine their impact on results and fitting stability for two sets of three-dimensional spectra acquired with metabolite-cycled PRESS from cerebral gray and white matter locations. One dataset was used for model optimization, and also examining the influence of prior knowledge on estimated parameters. The most promising model was applied to a second dataset. It turned out that the mono-exponential decay model appears to be inadequate to represent TE-dependent signal features of the MMBG. TE-adapted MMBG spectra were therefore determined. For a reliable overall quantification of implicated metabolite concentrations and relaxation times, a general fitting model had to be constrained in terms of the number of fitting variables and the allowed parameter space. With such a model in place, fitting precision for metabolite contents and relaxation times was excellent, while fitting accuracy is difficult to judge and bias was likely influenced by the type of fitting constraints enforced. In summary, the parameterization of metabolite and macromolecule contributions in interrelated MR spectra has been examined by using multidimensional modeling on complex 2DJ-IR datasets. A tightly restricted model allows fitting of individual subject data with high fitting precision documented in small Cramér-Rao lower bounds, good repeatability values and a relatively small spread of estimated concentration and relaxation values for a healthy subject cohort.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR)
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology > DCR Magnetic Resonance Spectroscopy and Methodology (AMSM)
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > Forschungsbereich Pavillon 52 > Abt. Magnetresonanz-Spektroskopie und Methodologie, AMSM

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Höfemann, Maike Svenja; Bolliger, Christine; Chong, Daniel and Kreis, Roland

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1099-1492

Publisher:

Wiley

Funders:

[4] Swiss National Science Foundation

Language:

English

Submitter:

Maria de Fatima Henriques Bernardo

Date Deposited:

03 Jul 2020 16:23

Last Modified:

30 Jul 2020 06:18

Publisher DOI:

10.1002/nbm.4328

PubMed ID:

32542861

Uncontrolled Keywords:

1H MR spectroscopy fitting constraints macromolecules modeling multidimensional fitting prior knowledge quantification simulation of spectra

BORIS DOI:

10.7892/boris.144735

URI:

https://boris.unibe.ch/id/eprint/144735

Actions (login required)

Edit item Edit item
Provide Feedback