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Abstract:
Proton magnetic resonance spectroscopy (1H-MRS) allows non-invasive quantification of
the human brain's metabolism in vivo. 1H-MRS measures the interaction of the 1H-
hydrogen isotope with oscillating electromagnetic fields in the presence of a strong
electromagnetic field. The measured MRS signal of the 1H-hydrogen atoms reflects the
concentration of the metabolites present in the tissue. Metabolites are small molecules
reflecting the metabolism.
Each 1H-hydrogen atom present in a metabolite has a specific resonance frequency,
which depends on the chemical structure of the metabolite. The ensemble of the
resonance frequencies of all metabolites present in the measured tissue creates the MRS
signal. The MRS signal is Fourier transformed, producing an MRS spectrum, where each
resonance frequency appears as a distinct peak. The most abundant molecule in the
human tissue is water. The resonance frequency of water is suppressed in 1H-MRS to
permit the quantification of other metabolites, which are present with significantly lower
concentrations. In the MRS spectrum, protons with lower resonance frequencies than
water form the upfield spectrum, whereas protons with higher resonance frequencies form
the downfield spectrum.
This work focused on the modelling of the MRS spectrum. The first part is focused on the
accurate determination of metabolite concentrations.
The upfield spectrum contains most brain metabolites of clinical interest. However, there
is a severe spectral overlap between the metabolite resonances, and therefore dedicated
software calculates the contributions of individual metabolites. The modelling of the
individual metabolite contributions to the measured spectrum is referred to as spectral
fitting. Through this spectral fitting, the metabolite concentrations needed for clinical
diagnostics are determined.
The most significant overlap in MRS spectra originates from the signals underlying the
metabolite resonances, referred to as the macromolecular spectrum. The
macromolecular spectrum contains the resonance frequencies of protons in proteins and
peptides, which have a slightly faster signal decay than the smaller molecules
(metabolites). Other contributors to the spectral overlap are residuals of the not entirely suppressed
water signal or lipid signals originating from outside the volume of interest. A spline
baseline is typically used in the fitting software to model these contributors.
This work firstly investigated the impacts of different macromolecular spectra and spline
baselines used in spectral fitting. Significant effects in the quantified metabolite
concentrations were noticed, when the spline baseline flexibility was altered in the
community “gold standard” software, LCModel. Therefore, the newly developed fitting
algorithm proposed in this work, ProFit-v3, incorporates an automatic adaptive baseline
flexibility determination. The ProFit-v3 software was then systematically evaluated to
different perturbations and baseline effects. The quantified concentrations were
compared to the ground truth (when known) and the LCModel software results.
The second part of this work focuses on the modelling of the less investigated regions of
the MRS spectrum.
The downfield spectrum contains many resonance peaks unassigned to metabolite
contributions. In this work, downfield spectral peaks were used to quantify intracellular
pH. Additionally, for all downfield peaks T2 relaxation times, peak linewidths, and
concentrations were calculated. Lastly, based on the quantified peak properties combined
with previous literature measurements, the contributing molecules to the downfield peaks
were assigned.
The macromolecular spectrum was attributed by previous literature to contributions of
amino acids in proteins and peptides, based on in vitro measurement of dialyzed cytosol.
Moreover, the resonance frequencies of protein amino acids have been extensively
collected into a protein database by the NMR community. Hence, this work proposes a
modelling approach to quantify the in vivo measured macromolecular spectrum to
individual amino acids.
In conclusion, the investigation results and the proposed fitting software ProFit-v3 from
this work should lead to improved quantification of 1H-MRS spectra. Lastly, the peak
assignments in the downfield spectra and the proposed amino acid model promises
possible future biomarkers for disease.
