Comparison of MRI Spectroscopy software packages performance and application on HCV-infected patients’ real data

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Sala Llonch, Roser, Laredo Gregorio, Carlos1H MRS is conceived as a pioneer methodology for brain metabolism inspection and health status appraisal. Post-processing interventions are required to obtain explicit metabolite quantification values from which to derive diagnosis. On the grounds of addressing and covering such operation, multiple software packages have been recently developed and launched leading to an amorphous assortment of spectroscopic image processing tools, with lack of standardization and regulation. The current study thereby intends to judge the coherence and consistency of compound estimation outputs in terms of result variability by intercorrelation and intracorrelation analyses between appointed programs, being LCModel, Osprey, TARQUIN, and spant toolbox. The examination is performed on a 83-subject SVS short-TE 3T SIEMENS PRESS spectroscopic acquisitions’ collection, including healthy controls and HCV-infected patients assisted with DAA treatment. The analytical core of the project assesses software performance through the creation of a Python script in order to automatically compute and display the results sought. The statistical tests providing enough information to draw substantial conclusions stem from extraction of coefficient of determination (R2 ), Pearson’s coefficient (r), and intraclass correlation coefficient (ICC) together with representation of boxplots, rainclouds, and scatter plots easing data visualization. A clinical implementation is also entailed on the same basis, whose purpose is to reveal actual DAA treatment effect on HCV-infected patients by means of metabolite concentration alteration and hypothetical restoration. Conclusions declare evident and alarming variability among MRS platforms compromising the rigor, sharpness and systematization demanded in this discipline since quantification results hold incoherences, although they do not seem to affect or oppose medical determinations jeopardizing patient’s health. However, it would be interesting to extend the analysis to a greater cohort of subjects to reinforce and get to more solid resolutions

Comparison of seven modelling algorithms for γ-aminobutyric acid–edited proton magnetic resonance spectroscopy

Edited MRS sequences are widely used for studying γ-aminobutyric acid (GABA) in the human brain. Several algorithms are available for modelling these data, deriving metabolite concentration estimates through peak fitting or a linear combination of basis spectra. The present study compares seven such algorithms, using data obtained in a large multisite study. GABA-edited (GABA+, TE = 68 ms MEGA-PRESS) data from 222 subjects at 20 sites were processed via a standardised pipeline, before modelling with FSL-MRS, Gannet, AMARES, QUEST, LCModel, Osprey and Tarquin, using standardised vendor-specific basis sets (for GE, Philips and Siemens) where appropriate. After referencing metabolite estimates (to water or creatine), systematic differences in scale were observed between datasets acquired on different vendors' hardware, presenting across algorithms. Scale differences across algorithms were also observed. Using the correlation between metabolite estimates and voxel tissue fraction as a benchmark, most algorithms were found to be similarly effective in detecting differences in GABA+. An interclass correlation across all algorithms showed single-rater consistency for GABA+ estimates of around 0.38, indicating moderate agreement. Upon inclusion of a basis set component explicitly modelling the macromolecule signal underlying the observed 3.0 ppm GABA peaks, single-rater consistency improved to 0.44. Correlation between discrete pairs of algorithms varied, and was concerningly weak in some cases. Our findings highlight the need for consensus on appropriate modelling parameters across different algorithms, and for detailed reporting of the parameters adopted in individual studies to ensure reproducibility and meaningful comparison of outcomes between different studies.publishedVersio

New methods in quantification and RF pulse optimisation for magnetic resonance spectroscopy

Magnetic Resonance Spectroscopy (MRS) is a powerful medical diagnostic and research tool that enables us to identify metabolite concentrations in a region of interest (ROI) in-vivo. This non-invasive diagnostic technique provides a large amount of information about a certain region in the body, such as the brain or spinal cord, with no impact on patient wellbeing. MRS is readily available in many clinical units across the UK with an MRI machine and no additional hardware is needed. MRS has a number of challenges, including the requirement of a much higher level of magnetic field calibration compared to MRI, and detecting and analysing a substantially weaker signal per metabolite. To complicate the matter, there is a broad range of metabolites found in-vivo with overlapping proton spectra, obscuring signals and making spectral analysis very challenging. The primary focus of this thesis is to explore methods to aid quantification of metabolites by exploring two ends of the issue, focusing specifically on GABA, NAA, Creatine quantification, of interest to a range of neuroscience studies. Firstly, the focus is on the analysis of the acquired spectral data utilizing the MEGA-PRESS pulse sequence, specifically aimed at GABA. Comprehensively benchmarking the current state-of-the-art spectral quantification methods with experimental data from phantoms of known composition lays the foundation for devising an improved quantification technique. This novel quantification method utilises a convolutional neural network for MEGA-PRESS spectra and can outperform the state-of-the-art. Secondly, an optimisation method to find RF pulses that create specific excitations in the metabolites is devised, leading to spectra that are simpler to analyse. Such customisation of the spectra allows the removal of overlapping or obscuring features, creating chemically selective spectral acquisition methods. Moreover, the RF pulses are optimised over a range of scanner uncertainties to improve robustness. Simulations demonstrate that this approach can separate GABA, NAA and Creatine as well as Glutamine and Glutamate at 3 Tesla

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

Forecasting the quality of water-suppressed (1) H MR spectra based on a single-shot water scan.

PURPOSE To investigate whether an initial non-water-suppressed acquisition that provides information about the signal-to-noise ratio (SNR) and linewidth is enough to forecast the maximally achievable final spectral quality and thus inform the operator whether the foreseen number of averages and achieved field homogeneity is adequate. METHODS A large range of spectra with varying SNR and linewidth was simulated and fitted with popular fitting programs to determine the dependence of fitting errors on linewidth and SNR. A tool to forecast variance based on a single acquisition was developed and its performance evaluated on simulated and in vivo data obtained at 3 Tesla from various brain regions and acquisition settings. RESULTS A strong correlation to real uncertainties in estimated metabolite contents was found for the forecast values and the Cramer-Rao lower bounds obtained from the water-suppressed spectra. CONCLUSION It appears to be possible to forecast the best-case errors associated with specific metabolites to be found in model fits of water-suppressed spectra based on a single water scan. Thus, nonspecialist operators will be able to judge ahead of time whether the planned acquisition can possibly be of sufficient quality to answer the targeted clinical question or whether it needs more averages or improved shimming. Magn Reson Med, 2016. © 2016 International Society for Magnetic Resonance in Medicine

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

Forecasting the quality of water-suppressed 1

PURPOSE To investigate whether an initial non-water-suppressed acquisition that provides information about the signal-to-noise ratio (SNR) and linewidth is enough to forecast the maximally achievable final spectral quality and thus inform the operator whether the foreseen number of averages and achieved field homogeneity is adequate. METHODS A large range of spectra with varying SNR and linewidth was simulated and fitted with popular fitting programs to determine the dependence of fitting errors on linewidth and SNR. A tool to forecast variance based on a single acquisition was developed and its performance evaluated on simulated and in vivo data obtained at 3 Tesla from various brain regions and acquisition settings. RESULTS A strong correlation to real uncertainties in estimated metabolite contents was found for the forecast values and the Cramer-Rao lower bounds obtained from the water-suppressed spectra. CONCLUSION It appears to be possible to forecast the best-case errors associated with specific metabolites to be found in model fits of water-suppressed spectra based on a single water scan. Thus, nonspecialist operators will be able to judge ahead of time whether the planned acquisition can possibly be of sufficient quality to answer the targeted clinical question or whether it needs more averages or improved shimming. Magn Reson Med, 2016. © 2016 International Society for Magnetic Resonance in Medicine