Quantitative Magnetic Resonance Spectroscopy of Brain Metabolites and Macromolecules at Ultra-High Field

Die Protonen-Magnetresonanzspektroskopie (1H-MRS) ist eine nicht-invasive Technik, die die Untersuchung der neurochemischen Zusammensetzung des menschlichen Gehirns ermöglicht. Bedeutende klinische Anwendungen von 1H-MRS ergaben sich in der Diagnose von Erkrankungen, in dem Verständnis von Krankheitsmechanismen oder in der Behandlungsüberwachung. Die zuverlässige Erkennung und Quantifizierung der Metaboliten ist von größter Bedeutung, um Biomarker für verschiedene neurologische Krankheiten zu etablieren. Zusätzlich enthalten Makromoleküle, die in dem Protonen-Spektrum breite Spektrallinien unter dem Metaboliten-Spektrum bilden, zahlreiche, wertvolle Informationen. Die Spektrallinien der Makromoleküle stammen von Aminosäuren aus Proteinen und Peptiden des Cytosols. Frühere Studien haben die klinische Relevanz von Makromolekülen in Erkrankungen wie Multiple Sklerose, Tumoren oder chronisch- traumatische Enzephalopathie gezeigt. Jedoch müssen mehrere Charakteristiken der Makromoleküle noch erforscht werden. Ein tiefgehendes Verständnis der Makromoleküle könnte dabei die Entdeckung neuer Biomarker für neurologische Krankheiten ermöglichen. Zusätzlich kann die Charakterisierung der makromolekularen Spektrallinien helfen folgende offene Fragen der MR Spektroskopie zu beantworten: den biologischen Ursprung der einzelnen makromolekularen Spektrallinien, die Zuordnung der makromolekularen Spektrallinien zu einzelnen Aminosäuren sowie die Untersuchung von anderen möglichen Beiträgen zum Signal der Makromoleküle wie z.B. verschiedene Zucker, DNA oder RNA. Die Sensitivität von MRS wurde durch stärkere Magnetfelder erheblich verbessert. MRS Messungen am Ultrahochfeld (≥7 T) profitieren von einem höheren Signal-Rausch- Verhältnis und einer höheren spektralen Auflösung. Zusätzlich wurden Lokalisierungsmethoden und Quantifizierungsmethoden weiterentwickelt, die es ermöglichen, die Konzentrationen auch der Metaboliten und Makromoleküle akkurat zu bestimmen, die ein kleines Signal-Rausch-Verhältnis haben oder komplexere spektrale Muster aufgrund von J-Kopplung aufweisen. Im Fokus des ersten Teils dieser Doktorarbeit steht die Charakterisierung der physikalischen Eigenschaften der makromolekularen Spektrallinien und die Frage, wie diese das Metaboliten-Spektrum beeinflussen. Dazu wurden Spektren am 9.4 T im menschlichen Gehirn aufgenommen, um hiermit T2 Relaxationszeiten zu bestimmen bzw. Linienbreiten quantitativ zu analysieren. Diese Analysen liefern Erkenntnisse über die spektrale Überlappung und J-Kopplungseffekte, die man in den makromolekularen Spektrallinien beobachtet. Zusätzlich wird eine neue „double inversion recovery“ Methode vorgestellt, um damit die T1 Relaxationszeiten von einzelnen makromolekularen Spektrallinien zu bestimmen. Der zweite Teil dieser Doktorarbeit beschäftigt sich mit der Quantifizierung von den Metaboliten des menschlichen Gehirns am 9.4 T mittels ein- und zweidimensionaler MRS Methoden. Die Konzentrationen der Metaboliten werden in mmol/kg berichtet. Hierbei wurden T1- und T2-Gewichtungen korrigiert sowie die Zusammensetzung des gemessenen Gewebes berücksichtigt. Die resultierenden Konzentrationen, die mittels der zwei Methoden gemessen wurden, werden untereinander sowie mit weiterer Literatur verglichen.Proton magnetic resonance spectroscopy (1H MRS) in the human brain is a non-invasive technique capable of aiding the investigation of the neurochemical composition. The clinical importance of 1H MRS can be seen in pathological diagnosis, understanding disease mechanisms or in treatment monitoring. Reliable detection and quantification of metabolites is of paramount importance in establishing potential biomarkers for several neurological pathologies. Furthermore, broad macromolecular resonances underlying metabolite peaks in a proton spectrum also hold a wealth of information. These macromolecular resonances originate from amino acids within cytosolic peptides and proteins. Some studies in the past have even discussed their clinical relevance in pathologies such as acute multiple sclerosis, glioma, and traumatic encephalopathy. However, the characteristics of these macromolecular resonances are yet to be fully explored. In-depth knowledge about the macromolecules could open up a new horizon of potential biomarkers for neurological diseases. In addition, characterizing macromolecular resonances may help the MR community answer some of the lingering research questions such as identifying the biological background of the individual macromolecular peaks, assigning macromolecular peaks to particular amino acids, and investigating other contributions to the macromolecular signal such as sugars, DNA or RNA. Detection capabilities of MRS have increased to a great extent with increasing static magnetic field. Ultra-high field (≥7 T) MRS benefits from increased signal-to-noise ratio (SNR) and improved spectral resolution. There is also constant development in localization techniques and quantification methods to accurately measure concentrations of metabolites and macromolecules with lower signal-to-noise ratio and complex spectral pattern due to J-coupling. The first part of the thesis focuses on characterizing the physical properties of macromolecular resonances in the human brain at 9.4 T and understanding their contribution to the metabolite spectrum. T2 relaxation times are calculated and a quantitative linewidth analysis is performed to understand the degree of overlap and J- coupling effects in the observed macromolecular peaks. Moreover, a novel double inversion recovery method is proposed to determine T1 relaxation times of individual macromolecular resonance lines. The second part of the thesis focuses on quantification of metabolites in the human brain at 9.4 T using one-dimensional and two-dimensional MRS techniques. Metabolite concentrations are reported in millimoles/kg after correcting for T1- and T2-weighting effects and the tissue composition. The concentration values measured from both the acquisition techniques were compared against each other and to literature

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

Temperature and pH Imaging using Chemical Exchange Saturation Transfer (CEST) MRI Contrast

Chemical exchange saturation transfer (CEST) is a novel mechanism used to generate contrast in magnetic resonance imaging (MRI). Recently, CEST contrast was proposed to noninvasively measure physiological parameters including temperature and pH. Tissue temperature and pH are known markers of pathological processes in many diseases including stroke and cancer. CEST contrast can be generated using endogenous proteins and peptides (endogenous CEST) or using exogenous paramagnetic lanthanide agents (PARACEST). The general problem of optimizing applications of endogenous CEST and PARACEST contrast to measure temperature and pH is addressed in this thesis. Highlights of the thesis include a novel application of PARACEST contrast to measure extracellular pH and temperature in-vivo and a novel ratiometric approach that uses endogenous CEST contrast to measure intracellular pH in-vivo. Using a Tm3+-based PARACEST agent (Tm3+-DOTAM-Gly-Lys), the PARACEST amide peak chemical shift and linewidth were shown to depend on pH and temperature in a deterministic manner. Quantitative temperature and pH maps were simultaneously measured in a normal mouse leg following agent injection using empirical relations derived in-vitro. A ratio of endogenous amide and amine proton CEST effects was developed to measure absolute tissue pH that is heavily weighted to the intracellular compartment. The technique called amine and amide concentration-independent detection (AACID) was developed using in-vitro phantoms and numerical simulations. Following in-vivo pH-calibration using 31P-magnetic resonance spectroscopy (MRS), tissue pH measurement was demonstrated in mice following focal cerebral ischemia. Local acidosis was measured in ischemic regions and found to correlate with regions of tissue damage. Finally, two endogenous CEST metrics including the AACID ratio were used to monitor cancer treatment using an anticancer drug called lonidamine. Lonidamine selectively acidifies cancer cells. In-vivo experiments demonstrate that endogenous CEST imaging is sensitive to intracellular acidification by lonidamine in a glioblastoma brain tumor mouse model. Overall, the results presented in this thesis demonstrate quantitative measurement of pH and temperature using CEST and/or PARACEST contrast in-vivo. Some of the novel techniques developed in this thesis were demonstrated in stroke and cancer mouse models. Future work should focus on 1) development of PARACEST agents with higher sensitivity in-vivo to improve accuracy of temperature and pH maps; 2) application of AACID for absolute pH measurement to differentiate high- and low-grade tumors in-vivo; and 3) application of endogenous CEST measurement to monitor tumor response to different clinically approved chemotherapy treatments

Chemical Exchange Saturation Transfer Imaging Of Endogenous Metabolites For Monitoring Oxidative Phosphorylation And Glycolysis In Vivo

Oxidative phosphorylation (OXPHOS) and glycolysis are two cellular metabolic pathways that play a crucial role in the functions of biological systems. Currently, magnetic resonance spectroscopy (MRS) (13C, 31P, and 1H) and positron emission tomography (PET) methods are used to investigate changes in these pathways that result from metabolic dysfunction. However, MRS methods are limited by low resolution and long acquisition times. While 18F-fluoro-2-deoxy-D-glucose (18F-FDG) PET is a widely used clinical modality, it requires the use of radioactive ligands. Thus, there is an unmet need for techniques to image these metabolic processes noninvasively, and with higher resolution in vivo. In this dissertation, we exploited the chemical exchange saturation (CEST) phenomenon to develop and optimize endogenous CEST magnetic resonance imaging (MRI) methods to measure OXPHOS and glycolysis, and demonstrated application of those techniques to study impaired metabolism in vivo. These CEST methods offer several orders of magnitude higher sensitivity compared to traditional spectroscopic techniques. Recently developed CEST imaging of free creatine (CrCEST) was targeted as a means of measuring OXPHOS. We optimized and validated this technique in healthy human skeletal muscle, showing that CrCEST imaging in dynamic exercise studies provides a measure of the mitochondrial rate of OXPHOS. CrCEST imaging was then implemented in a cohort of subjects affected by genetic disorders of the mitochondria. The results of these studies demonstrate that CrCEST has the capability to distinguish between healthy and impaired OXPHOS in muscle. In some diseases with altered metabolism, like cancer, aerobic glycolysis dominates, leading to increased lactate production. Existing methods for imaging lactate in vivo involve expensive, radiolabeled tracers. In this work, we demonstrated the feasibility of imaging lactate with CEST (“LATEST”) in phantoms with physiological concentrations. Then, we validated the method dynamically in vivo by measuring lactate production and clearance in intensely exercised human skeletal muscle, which utilizes anaerobic glycolysis. Finally, we infused rats bearing lymphoma tumors with non-labeled pyruvate and demonstrated the ability of LATEST MRI to image tumors and measure dynamic lactate changes over time. Together, these studies demonstrate that metabolic processes can be monitored in vivo using CEST MRI, with potential for widespread clinical applications

Detecting neuroinflammation with molecular MRI

The work in this thesis is focused on the study of neuroinflammation with molecular magnetic resonance imaging (MRI) methods. Neuroinflammation is a response of the central nervous system to pathological insult and it is present in many neurological disorders, such as Alzheimer’s disease. Being able to image neuroinflammation non-invasively with MRI techniques would have an important clinical value for diagnosis and assessment of therapy effectiveness. The aim of this work is to develop and validate an MR biomarker of neuroinflammation using MR Spectroscopy (MRS) and chemical exchange saturation transfer imaging (CEST). First, intravenous administration of lipopolysaccharide (LPS) is used as a mild inflammatory stimulus in wild type mice and in a mouse model of Alzheimer’s disease (AD). Elevated levels of the osmolyte myo-inositol, measured with MRS and microglia activation are found in AD mice after LPS administration. Due to the inherent low spatial resolution of MRS, a CEST MRI method is developed next. A myo-inositol CEST protocol is optimised, using Matlab simulations based on the Bloch-McConnell equations for a three pool model, in order to maximize the contrast and to estimate the amount of signal that can be expected in vivo. In vitro and in vivo tests are presented and a fast CEST sequence is developed, while the experimental difficulties and limitations of the technique are discussed. A CEST protocol is finally applied to evaluate the metabolite response to an LPS inflammatory challenge using MRS and histology as validation. A correlation is described between CEST and MRS myo-inositol levels, as well as between CEST and microglia concentration (Iba1 immunostaining), which highlight the potential of CEST as a non-invasive in vivo neuroinflammatory biomarker