Quantification in short echo-time magnetic resonance spectroscopic imaging at 7 Tesla in healthy and diseased brain

1H Magnetic Resonance Spectroscopic Imaging (MRSI) ist eine nichtinvasive Methode die Prinzipen von Magnetic Resonance Spectroscopy (MRS) und Magnetic Resonance Imaging (MRI) miteinander verbindet. Dies erlaubt es, direkte Einblick in die biochemischen Prozesse in Menschen und Tieren zu nehmen. Alle MR-Methoden leiden unter relativ geringer Sensitivität, da nur ein Bruchteil der 1 H-Kerne zum messbaren MR-Signal beiträgt. Daraus ergibt sich, dass die räumliche und zeitliche Auflösung jeder MR-Messung von dem Signal-zu-Rauschen-Verhältnis (SNR) abhängt. SNR nimmt generell mit höheren Feldstärken (B0) zu, ein Umstand der in den letzten Jahren zu einem Trend in Richtung Ultra-High Field (UHF, i.e. 7T) MR Systemen geführt hat. 1 H-MRSI des Gehirns kann von UHF profitieren, benötigt aber neue Ansätze, um die technischen Herausforderungen, die sich mit den höheren Feldstärken ergeben, zu lösen. 1H-MRSI Sequenzen mit direkter FID-Akquisition (FID-MRSI) wurden vorgeschlagen um den Signalverlust zu reduzieren und um die Auflösung zu erhöhen. Obwohl sie für klinische Anwendungen vielversprechend sind ist die Quantifizierung der Resultate schwierig. Adäquate Qualitätskontrolle ist notwendig, aber die anfallenden Datenmengen sind weitaus größer als bei konventioneller MRS. Hinzu kommt dass die Metabolitensignale mit Signalen von Makromolekülen (MM) mit hohem Molekulargewicht überlappen, was die Quantifizierung verzerren kann. Daher war es der Hauptzweck der in dieser Arbeit präsentierten Forschung, die Quantifizierung von UHF-1H-FID-MRSI zu verbessern. Die entwickelte Software kann mit minimalem Anwenderaufwand MRSI-Daten prozessieren und zuverlässige Qualitätssicherung bieten. Zusätzlich wurde selektive ignalreduktion mithilfe eines Double Inversion Recovery Moduls implementiert. Dies ermöglichte die Messung von Metabolit-freien MM-Spektren des menschlichen Gehirns, die in das Vorwissen des Fitting-Algorithmus integriert werden konnten. Dafür wurde ein durchschnittliches in vivo MM Spektrum in neun einzelne Komponenten parametrisiert. Damit war es uns zum ersten Mal möglich, die Verteilung dieser MM-Komponenten im Gehirn mit einem nominalen Voxelvolu-men von 0.1 ml als Bild darzustellen. Dieses parametrisierte MM-Modell erlaubte es ebenfalls, FID-MRSI in Multiple Sklerose-Patienten (MS) zu quantifizieren. Sonst nicht erkennbare Unterschiede in der MM-Zusammensetzung zwischen MS-Läsionen und gesundem Gewebe konnten identifiziert werden. Die Erfahrungen aus dem UHF-Bereich konnten ebenfalls für GABA-editiertes MRSI bei 3T verwendet werden. Die Resultate dieser Arbeit erlauben es manche der Hindernisse für die Quantifizierung von FID-MRSI zu überwinden und erlauben zukünftige klinische Anwendungen. Ein universales MM-Model wurde entwickelt, um FID-MRSI Daten von gesunden und kranken menschlichen Gehirnen zu quantifizieren. Mögliche pathologische Veränderungen vonMM in MS wurden beschrieben, eine gründlichere Untersuchung ist hier jedoch noch nötig.1H magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique that merges the principles of magnetic resonance spectroscopy (MRS) and magnetic resonance imaging (MRI). It provides valuable insights into biochemical processes inside the human (or animal) body. All MR-based techniques suffer from low sensitivity, because only a small fraction of 1H nuclei is contributing to the MR-detectable signal. In this respect, the temporal and spatial resolution of an MR experiment is always influenced by the available signal-to-noise ratio (SNR). In general, SNR increases with a higher static magnetic field (B0). Therefore, there has been a trend towards installation of ultra-high field (UHF, i.e. B07T) MR systems. 1H-MRSI of the brain may benefit from the UHF, however novel approaches are necessary to overcome the technical challenges that arise at such high magnetic field strengths. 1H-MRSI sequences based on a direct FID acquisition (FID-MRSI) were proposed with minimal signal loss and high spatial resolution. These sequences have proved to be very promising for clinical applications, however their quantification is troublesome. An adequate quality control has to be ensured, because the amount of data to be quantified is higher compared to conventional MRS. Moreover, the signal of metabolites is overlapping with the signal of high-molecular weight macromolecules (MM) that bias their quantification. The main purpose of this thesis was to improve the quantification of 1H-FID-MRSI data measured in the brain at UHF. The developed software with minimal user interaction facilitated the post-processing and provided reliable quality assurance of the data. A double inversion recovery module was implemented into our FID-MRSI sequence in order to achieve a selective signal nulling. This enabled a detection of metabolite-free MM spectra from the human brain that were further included into the prior knowledge of the spectral fitting algorithm. Consequently, an averaged in vivo MM spectrum was parameterized into nine individual MM components. For the first time, we were able to map the individual MM contributions in the healthy human brain with a nominal voxel volume of 0.1 ml. The parameterized MM model allowed for quantification of FID-MRSI data measured from Multiple Sclerosis (MS) patients. Additionally, MM differences were found between MS lesions and healthy tissue that could not be detected without proper MM handling. The knowledge and experience from UHF was also utilized at 3T in GABA-edited MRSI. The results presented in this work remove some of the major obstacles that hindered the quantification of FID-MRSI data and help to promote further clinical applications. A universal model of MM was created that can serve for quantification of FID-MRSI data acquired from both healthy and diseased human brain. Possible pathological changes of MM were outlined in MS. This work represents the first step for reliable quantification of the brain metabolites at UHF.submitted by Michal PovazanZusammenfassung in deutscher SpracheAbweichender Titel laut Übersetzung der Verfasserin/des VerfassersMedizinische Universität Wien, Dissertation, 2017OeB

NMR in Biomedicine / Spatial variability and reproducibility of GABA-edited MEGA-LASER 3D-MRSI in the brain at 3T

The reproducibility of gamma-aminobutyric acid (GABA) quantification results, obtained with MRSI, was determined on a 3T MR scanner in healthy adults. In this study, a spiral-encoded, GABA-edited, MEGA-LASER MRSI sequence with real-time motion-scanner-instability corrections was applied for robust 3D mapping of neurotransmitters in the brain. In particular, the GABA(+) (i.e. GABA plus macromolecule contamination) and Glx (i.e. glutamate plus glutamine contamination) signal was measured. This sequence enables 3D-MRSI with about 3cm(3) nominal resolution in about 20min. Since reliable quantification of GABA is challenging, the spatial distribution of the inter-subject and intra-subject variability of GABA(+) and Glx levels was studied via test-retest assessment in 14 healthy volunteers (seven men-seven women). For both inter-subject and intra-subject repeated measurement sessions a low coefficient of variation (CV) and a high intraclass correlation coefficient (ICC) were found for GABA(+) and Glx ratios across all evaluated voxels (intra-/inter-subject: GABA(+) ratios, CV similar to 8%-ICC>0.75; Glx ratios, CV similar to 6%-ICC>0.70). The same was found in selected brain regions for Glx ratios versus GABA(+) ratios (CV varied from about 5% versus about 8% in occipital and parietal regions, to about 8% versus about 10% in the frontal area, thalamus, and basal ganglia). These results provide evidence that 3D mapping of GABA(+) and Glx using the described methodology provides high reproducibility for application in clinical and neuroscientific studies.KLI 61-B00(VLID)308377

Comparison of Multivendor Single-Voxel MR Spectroscopy Data Acquired in Healthy Brain at 26 Sites

Background: The hardware and software differences between MR vendors and individual sites influence the quantification of MR spectroscopy data. An analysis of a large data set may help to better understand sources of the total variance in quantified metabolite levels. Purpose: To compare multisite quantitative brain MR spectroscopy data acquired in healthy participants at 26 sites by using the vendor-supplied single-voxel point-resolved spectroscopy (PRESS) sequence. Materials and Methods: An MR spectroscopy protocol to acquire short-echo-time PRESS data from the midparietal region of the brainwas disseminated to 26 research sites operating 3.0-T MR scanners from three different vendors. In this prospective study, healthy participants were scanned between July 2016 and December 2017. Data were analyzed by using software with simulated basis sets customized for each vendor implementation. The proportion of total variance attributed to vendor-, site-, and participant-related effects was estimated by using a linear mixed-effects model. P values were derived through parametric bootstrapping of the linearmixed-effects models (denoted P-boot). Results: In total, 296 participants (mean age, 26 years +/- 4.6; 155 women and 141 men) were scanned. Good-quality data were recorded from all sites, as evidenced by a consistent linewidth of N-acetylaspartate (range, 4.4-5.0 Hz), signal-to-noise ratio (range,174-289), and low Cramer-Rao lower bounds ( .90), N-acetylaspartate and N-acetylaspartylglutamate (P-boot =.13), or glutamate and glutamine (P-boot =.11). Among the smaller resonances, no vendor effects were found for ascorbate (P-boot =.08), aspartate (P-boot >.90), glutathione (P-boot > .90), or lactate (P-boot =.28). Conclusion: Multisite multivendor single-voxel MR spectroscopy studies performed at 3.0 T can yield results that are coherent across vendors, provided that vendor differences in pulse sequence implementation are accounted for in data analysis. However, the site related effects on variability were more profound and suggest the need for further standardization of spectroscopic protocols. (C) RSNA, 202

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