Signal-to-noise per unit time optimization for in vivo single-voxel proton magnetic resonance spectroscopy of the brain: Theoretical formulation and experimental verification at two field strengths

Signal-to-noise ratio optimization, regarding repetition time selection, was explored mathematically and experimentally for single-voxel proton magnetic resonance spectroscopy. Theoretical findings were benchmarked against phantom measurements at 1.5 Tesla and localized in vivo proton brain spectra acquired at both 1.5 Tesla/3.0 Tesla. A detailed mathematical description of signal-to-noise ratio per unit time was derived, yielding an optimal repetition time of 1.256 times the metabolite longitudinal relaxation time. While long-repetition-time acquisitions minimize longitudinal relaxation time contributions, a repetition time of ~1.5s results in maximum signal-to-noise ratio per unit time, which can in turn be invested into smaller voxel sizes. The latter is of utmost importance in brain oncology, allowing accurate spectroscopic characterization of small lesions.Comment: 26 pages, 4 figures, submitted to Spectroscopy Letter

In vivo characterization of the downfield part of 1 H MR spectra of human brain at 9.4 T: Magnetization exchange with water and relation to conventionally determined metabolite content

PURPOSE: To perform exchange-rate measurements on the in vivo human brain downfield spectrum (5-10 ppm) at 9.4 T and to compare the variation in concentrations of the downfield resonances and of known upfield metabolites to determine potential peak labels. METHODS: Non-water-suppressed metabolite cycling was used in combination with an inversion transfer technique in two brain locations in healthy volunteers to measure the exchange rates and T1 values of exchanging peaks. Spectra were fitted with a heuristic model of a series of 13 or 14 Voigt lines, and a Bloch-McConnell model was used to fit the exchange rate curves. Concentrations from non-water-inverted spectra upfield and downfield were compared. RESULTS: Mean T1 values ranged from 0.40 to 0.77 s, and exchange rates from 0.74 to 13.8 s-1 . There were no significant correlations between downfield and upfield concentrations, except for N-acetylaspartate, with a correlation coefficient of 0.63 and P < 0.01. CONCLUSIONS: Using ultrahigh field allowed improved separation of peaks in the 8.2 to 8.5 ppm amide proton region, and the exchange rates of multiple downfield resonances including the 5.8-ppm peak, previously tentatively assigned to urea, were measured in vivo in human brain. Downfield peaks consisted of overlapping components, and largely missing correlations between upfield and downfield resonances-although not conclusive-indicate limited contributions from metabolites present upfield to the downfield spectrum. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine

Impact of ocean acidification (OA) on the acid-base regulation of polar cod: Time and localized tracking of brain pH changes

A consequence of accumulating carbon dioxide (CO2) in the atmosphere is global climate change. CO2 also dissolves in the ocean and reduces the water pH, resulting in ocean acidification (OA). Fish were long thought to be relatively resistant to pH changes in the water, because they possess an efficient acid-base regulation. However, recent findings indicate altered behaviour and changes in neurological processes in fish. To investigate the acid-base regulation in the brain of the polar cod (Boreogadus saida), a combination of two methods, based on the nuclear magnetic resonance (NMR) phenomenon, was used in this study. Non- localized 31P-NMR spectroscopy was used to measure the concentration of phosphometabolites. The intracellular pH (pHi) was calculated from the chemical shift of the 31 NMR signal of inorganic phosphate in relation to the phosphocreatine signal. spectroscopy is used to detect short-term changes in the concentration of different phosphometabolites and hence short-term changes in the pH. However, the determination of the pH value in a specific region as small as the fish brain is not possible with non-localized 31P- NMR spectroscopy. To verify the results of the measurements the chemical exchange saturation transfer (CEST) between taurine and water (TauCEST) was determined in a specific region in the brain of B. saida. Because CEST is pH dependant, changes in the CEST effect can give evidence on any pH changes with a higher spatial resolution than non-localized 31P-NMR spectroscopy. Additionally to pH changes, energy metabolism can be analysed with 31P-NMR spectroscopy by measuring the concentration of phosphometabolites such as inorganic phosphate, phosphocreatine or the three ATP subunits α-, β- and γ-ATP. After 20 hours of acclimatisation under control conditions, the animals were exposed to a CO2 concentration of 3500 ppm and a water pH of 6.92 ± 0.2 for four hours (hypercapnia). Then, the animals were tested again in water without elevated CO2 concentrations (pH 7.96 ± 0.3). The pHi decreased rapidly after switching to hypercapnia by a mean of 0.05 ± 0.2 and started to reach control values again after two hours. The maximum decrease in pHi was 0.17 and occurred in fish 2 and 4. Throughout the whole time of the experiment, there were no significant changes in the energy values

Amide proton signals as pH indicator for in vivo MRS and MRI of the brain: Responses to hypercapnia and hypothermia.

Using proton MRS and MRI of mouse brain at 9.4T, this work provides the first in vivo evidence of pH-dependent concurrent changes of three amide signals and related metabolic responses to hypercapnia and hypothermia. During hypercapnia, amide proton MRS signals of glutamine at 6.8-6.9ppm and 7.6ppm as well as of unspecific compounds at 8.1-8.3ppm increase by at least 50% both at 37°C and 22°C. These changes reflect a reduced proton exchange with water. They are strongly correlated with intracellular pH which ranges from 6.75±0.10 to 7.13±0.06 as determined from a shift in creatine phosphokinase equilibrium. In MRI, saturation transfer from aliphatic as well as aromatic and/or amide protons alters slightly during hypercapnia and significantly during hypothermia. The asymmetry in magnetization transfer ratios decreased slightly during hypercapnia and hypothermia. Regardless of pH or temperature, saturation transfer from aliphatic protons between -2 and -4ppm frequency offset to water protons is significantly greater than that from aromatic/amide protons at corresponding offsets between +2 and +4ppm. Irradiation of aliphatic compounds at -3.5ppm frequency offset from water predominantly saturates lipids and water associated with myelin. Taken together, the results indicate that, for the B1 power used in this study, dipolar coupling between aliphatic and water protons rather than proton exchange is the dominant factor in Z-spectra and magnetization transfer ratio asymmetry of the brain in vivo

Magnetic Resonance Spectroscopy: Quantitative Analysis of Brain Metabolites and Macromolecules

Die Protonen-Magnetresonanzspektroskopie (1H-MRS) ermöglicht die nichtinvasive in vivo Quantifizierung des Metabolismus im menschlichen Gehirn. In der 1H-MRS wird die Interaktion zwischen einem in ein starkes elektromagnetisches Feld platziertes 1H-Wasserstoffisotop und einem oszillierenden elektromagnetischen Feld gemessen. Die gemessenen MRS-Signale der 1H-Wasserstoffatome spiegeln die Konzentration der in dem Gewebe enthaltenen Metaboliten wieder. Jedes 1H-Wasserstoffatom in einem Metaboliten hat eine spezifische Resonanzfrequenz, die von der chemischen Struktur des Metaboliten abhängt. Die Gesamtheit der Resonanzfrequenzen aller Metaboliten in dem gemessenen Gewebe generiert das MRS-Signal. Durch die Fourier-Transformation dieses MRS-Signals entsteht ein MRS-Spektrum mit Spektrallinien, die den enthaltenen Resonanzfrequenzen entsprechen. Wasser ist das häufigste Molekül im menschlichen Gewebe. Um Metaboliten mit signifikant geringeren Konzentrationen quantifizieren zu können, wird in der MRS das Wassersignal unterdrückt. Wasserstoffatome mit einer niedrigeren Resonanzfrequenz als Wasser bilden das sogenannte „Upfield-Spektrum”, während die Wasserstoffatome mit einer höheren Resonanzfrequenz das “Downfield-Spektrum” bilden. Das „Upfield-Spektrum” enthält die Spektrallinien der meisten klinisch relevanten Metaboliten, ist aber von einer starken spektralen Überlagerung geprägt. Deshalb müssen die Anteile der einzelnen Metaboliten im Gesamtspektrum durch eine spezielle Software berechnet werden. Die Modellierung der einzelnen Beiträge der Metaboliten zu dem gemessenen Spektrum nennt man spektrales Fitting. Mithilfe des spektralen Fitting werden die für die klinische Diagnostik relevanten Metabolitenkonzentrationen bestimmt. Diese Doktorarbeit fokussiert sich auf die Modellierung des MRS-Spektrums. Der erste Teil beschäftigt sich mit der akkuraten Quantifizierung der Metabolitenkonzentrationen. Die signifikanteste spektrale Überlagerung im MRS entsteht durch Signale, die unter den Spektrallinien der Metaboliten liegen und die als makromolekulares Spektrum bezeichnet werden. Das makromolekulare Spektrum besteht aus den Resonanzfrequenzen der Protonen von Proteinen und Peptiden, deren MRS-Signal schneller zerfällt als das der kleineren Moleküle (Metaboliten). Zusätzlich tragen zu der spektralen Überlagerung nicht ausreichend unterdrückte Wassersignale, sowie Signale von Fettmolekülen, die sich von außerhalb des gemessenen Volumens in das Spektrum reinfalten bei. Diese unerwünschten Signale werden im spektralen Fitting typischerweise durch Spline-Grundlinien modelliert. In dieser Arbeit wird untersucht, wie sich verschiedene makromolekulare Spektren und Spline-Grundlinien auf das spektrale Fitting auswirken. Änderungen der Flexibilität an der Spline-Grundlinie im LCModel (am häufigsten genutzte MRS-Software) führen zu signifikant unterschiedlichen Metabolitenkonzentrationen. Deshalb wurde in dem für diese Arbeit neuentwickelten, spektralen Fitting-Algorithmus ProFit-v3 eine automatische Erkennung der notwendigen Flexibilität der Spline-Grundlinie etabliert. Die ProFit-v3 Software wurde danach systematisch auf verschiedene Perturbationen und Grundlinien getestet. Die quantifizierten Konzentrationen wurden mit den wahren Konzentrationen (falls bekannt) und mit den Ergebnissen der LCModel Software verglichen. Der zweite Teil dieser Arbeit untersucht neue Modellierungsmöglichkeiten für zwei weniger untersuchte Bereiche des MRS-Spektrums. Das „Downfield-Spektrum” enthält mehrere Spektrallinien, die noch keinen Metaboliten zugeordnet werden konnten. In dieser Arbeit wurde der intrazelluläre pH-Wert durch Downfield-Spektrallinien bestimmt. Im Weiteren wurden für alle Downfield-Spektrallinien T2 Relaxationszeiten, spektrale Linienbreiten und Konzentrationen berechnet. Zuletzt wurden die entsprechenden Metaboliten anhand der quantifizierten Eigenschaften und Messungen aus vorliegender Literatur zu den Spektrallinien zugeordnet. Vorherige Literatur ordnet das makromolekulare Spektrum Beiträge der Aminosäuren aus Proteinen und Peptiden zu. Zusätzlich wurden die Resonanzfrequenzen der Aminosäuren in Proteinen umfangreich von der NMR-Gemeinschaft in Proteindatenbanken gesammelt. Daher wird in dieser Arbeit ein Modellierungsverfahren vorgestellt, um die in vivo gemessenen makromolekularen Spektren als Kontribution einzelner Aminosäuren zu quantifizieren. Insgesamt konnte gezeigt werden, dass die Forschungsergebnisse und die vorgestellte ProFit-v3 Fitting Software zur Verbesserung der MRS Quantifizierung beitragen. Die Zuordnung von Metaboliten im „Downfield-Spektrum“ und das Modell zur Quantifizierung von Aminosäuren können als zukünftige Biomarker für Krankheiten dienen.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

Single-Voxel Proton Magnetic Resonance Spectroscopy in the Human Brain at 9.4 T: Methods and Applications

Proton magnetic resonance spectroscopy (1H-MRS) is a non-invasive method allowing the detection as well as the quantification of several metabolites in the human brain. The introduction of ultra-high field (UHF) scanners (≥7 T) led to an increase of the signal-to-noise ratio and a higher frequency dispersion, hence better spectral resolution. These advantages promote the potential of MRS. Despite the significant advantages of UHF for MRS, several technical challenges (such as B1+efficiency and inhomogeneity, increased power deposition, chemical shift displacement etc.) must be addressed for the efficient utilization of these prospective benefits. The methods and techniques developed during this Ph.D. demonstrated the feasibility of metabolite cycling (MC) at 9.4 T, and the advantages of non-water suppressed MRS regarding frequency and phase fluctuations. The newly developed sequences (MC-STEAM and MC-semi-LASER) enabled the acquisition of reliable spectra with enhanced frequency resolution, both upfield and downfield of water in 1H spectra. Furthermore, the designed RF coils, hardware setup (power splitters, phase cables, etc.), as well as, the gained knowledge regarding the achievement of efficient transmit fields and can be utilized in future MRS studies and applications. As a result, the human brain macromolecular baseline was investigated revealing additional macromolecular peaks and information regarding their concentration levels. Moreover, the chemical exchange rates of the downfield metabolites, as well as, their correlation with the upfield peaks were examined contributing further to the assignment of the downfield peaks. Finally, the performed functional MRS studies in which the MC-semi-LASER sequences were used, demonstrated the potentials of UHF and MC regarding the simultaneous investigation of water and metabolites alterations during visual stimulation