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

Investigation of the influence of macromolecules and spline baseline in the fitting model of human brain spectra at 9.4T

Purpose In this study, the influence of experimentally measured macromolecules and spline baseline on the quantification results of proton MRS data was investigated. Methods Proton MRS spectra from the left parietal lobe and the occipital lobe were acquired at 9.4T in the human brain using metabolite‐cycled semi‐LASER. Then, the left parietal lobe data, along with the occipital lobe, spectra were quantified and the influence of the inclusion of experimentally measured macromolecular basis sets in the fitting model was evaluated. Furthermore, the effect of the stiffness of the fitted spline baselines on the resulting metabolite concentrations was evaluated. Results In general, concentrations were higher for metabolites in occipital lobe than the left parietal lobe. The inclusion of an experimentally acquired measured macromolecular basis set from another brain region neither affected the quantification results nor the resulting spline baselines significantly. A highly flexible spline baseline led to overestimation or underestimation of metabolite concentrations. Differences of above 15% in the quantification of metabolite levels for both lobes were observed for several metabolites using LCModel default settings for spline baselines and macromolecules in comparison to stiffer spline baselines. Conclusion Fitting with the default LCModel macromolecular basis set and spline baseline model had significant influence in the resulting spline baselines, leading to large deviations both in the concentrations and fitted macromolecular components. The number of knots in the spline may create overflexible baselines, which can potentially lead to quantification errors. Interestingly, the interchange of macromolecular basis set between occipital lobe and left parietal lobe spectra had less influence on the quantification results compared to the default LCModel settings

センターの増改築にあたって

Proton MR spectra of the brain, especially those measured at short and intermediate echo times, contain signals from mobile macromolecules (MM). A description of the main MM is provided in this consensus paper. These broad peaks of MM underlie the narrower peaks of metabolites and often complicate their quantification but they also may have potential importance as biomarkers in specific diseases. Thus, separation of broad MM signals from low molecular weight metabolites enables accurate determination of metabolite concentrations and is of primary interest in many studies. Other studies attempt to understand the origin of the MM spectrum, to decompose it into individual spectral regions or peaks and to use the components of the MM spectrum as markers of various physiological or pathological conditions in biomedical research or clinical practice. The aim of this consensus paper is to provide an overview and some recommendations on how to handle the MM signals in different types of studies together with a list of open issues in the field, which are all summarized at the end of the paper