Exploring configuration-based relaxometry and imaging with balanced steady-state free precession

Over the years, magnetic resonance (MR) imaging has become a fundamental part of the diagnostic process in hospitals worldwide. While the underlying physics dates back more than 60 years with the development of nuclear magnetic resonance, methods that aim to accurately measure the multitude of parameters governing the signal formation are still a topic of active research and developments today. The main aim of this work is to explore the possibilities of developing quantification techniques based on a particular type of MR acquisitions: balanced Steady-State Free Precession (bSSFP). The first chapter briefly introduces the most relevant basic concepts of MR physics that will serve as foundation for the development of the methods presented thereafter. In the second chapter, a new method using multiple bSSFP scans is presented that aim to achieve motion insensitive three-dimensional quantification of relaxation times and thereby improve a recently published technique based on unbalanced gradient echo acquisitions. The method is then evaluated both in phantoms and in vivo studies at 3 T and the results discussed. These include an interesting bias of the method that might provide useful insights into the underlying tissue micro-structure. One the major challenges with bSSFP imaging is the presence of dark regions inside the images, which are due to inhomogeneities in the main magnetic field. In the third chapter, a new approach to address those issues is proposed, termed trueCISS, which combines fast imaging using sparse sampling with compressed sensing reconstructions and multi-parametric fitting, which ultimately allows the synthesis of artefact-free images. Evaluation of the new method is done at 3 T for the human brain. Finally, an extension of the trueCISS technique is presented in chapter four, where the process used to model the data to the signal equation is replaced by a novel algorithm based on configuration theory, which is essentially a representation of the signal formation processes in the Fourier domain. The improved trueCISS imaging method is then successfully evaluated with measurements at ultra high field strengths such as 7 T and 9.4 T which demonstrate the advantages of the new approach

ANISOTROPIC POLARIZED LIGHT SCATTER AND MOLECULAR FACTOR COMPUTING IN PHARMACEUTICAL CLEANING VALIDATION AND BIOMEDICAL SPECTROSCOPY

Spectroscopy and other optical methods can often be employed with limited or no sample preparation, making them well suited for in situ and in vivo analysis. This dissertation focuses on the use of a near-infrared spectroscopy (NIRS) and polarized light scatter for two such applications: the assessment of cardiovascular disease, and the validation of cleaning processes for pharmaceutical equipment.There is a need for more effective in vivo techniques for assessing intravascular disorders, such as aortic aneurysms and vulnerable atherosclerotic plaques. These, and other cardiovascular disorders, are often associated with structural remodeling of vascular walls. NIRS has previously been demonstrated as an effective technique for the analysis of intact biological samples. In this research, traditional NIRS is used in the analysis of aortic tissue samples from a murine knockout model that develops abdominal aortic aneurysms (AAAs) following infusion of angiotensin II. Effective application of NIRS in vivo, however, requires a departure from traditional instrumental principles. Toward this end, the groundwork for a fiber optic-based catheter system employing a novel optical encoding technique, termed molecular factor computing (MFC), was developed for differentiating cholesterol, collagen and elastin through intervening red blood cell solutions. In MFC, the transmission spectra of chemical compounds are used to collect measurements directly correlated to the desired sample information.Pharmaceutical cleaning validation is another field that can greatly benefit from novel analytical methods. Conventionally cleaning validation is accomplished through surface residue sampling followed by analysis using a traditional analytical method. Drawbacks to this approach include cost, analysis time, and uncertainties associated with the sampling and extraction methods. This research explores the development of in situ cleaning validation methods to eliminate these issues. The use of light scatter and polarization was investigated for the detection and quantification of surface residues. Although effective, the ability to discriminate between residues was not established with these techniques. With that aim in mind, the differentiation of surface residues using NIRS and MFC was also investigated

Anthropometric and genetic determinants of cardiac morphology and function

Background Cardiac structure and function result from complex interactions between genetic and environmental factors. Population-based studies have relied on 2-dimensional cardiovascular magnetic resonance as the gold-standard for phenotyping. However, this technique provides limited global metrics and is insensitive to regional or asymmetric changes in left ventricular (LV) morphology. High-resolution 3-dimensional cardiac magnetic resonance (3D-CMR) with computational quantitative phenotyping, might improve on traditional CMR by enabling the creation of detailed 3D statistical models of the variation in cardiac phenotypes for use in studies of genetic and/or environmental effects on cardiac form or function. Purpose To determine whether 3D-CMR is applicable at scale, and provides methodological and statistical advantages over conventional imaging for large-scale population studies and to apply 3D-CMR to anthropometric and genetic studies of the heart. Methods 1530 volunteers (54.8% females, 74.7% Caucasian, mean age 41.3±13.0 years) without self-reported cardiovascular disease were recruited prospectively to the Digital Heart Project. Using a cardiac atlas-based software, these images were computationally processed and quantitatively analysed. Parameters such as myocardial shape, curvature, wall thickness, relative wall thickness, end-systolic wall stress, fractional wall thickening and ventricular volumes were extracted at over 46,000 points in the model. The relationships between these parameters and systolic blood pressure (SBP), fat mass, lean mass and genetic variationswere analysed using 3D regression models adjusted for body surface area, gender, race, age and multiple testing. Targeted resequencing of titin (TTN), the largest human gene and the commonest genetic cause of dilated cardiomyopathy, was performed in 928 subjects while common variants (~700.000) were genotyped in 1346 subjects. Results Automatically segmented 3D images were more accurate than 2D images at defining cardiac surfaces, resulting in fewer subjects being required to detect a statistically significant 1 mm difference in wall thickness. 3D-CMR enabled the detection of a strong and distinct regionality of the effects of SBP, body composition and genetic variation on the heart. It shows that the precursors of the hypertensive heart phenotype can be traced to healthy normotensives and that different ratios of body composition are associated with particular gender-specific patterns of cardiac remodelling. In 17 asymptomatic subjects with genetic variations associated with dilated cardiomyopathy, early stages of ventricular impairment and wall thinning were identified, which were not apparent by 2D imaging. Conclusions 3D-CMR combined with computational modelling provides high-resolution insight into the earliest stages of heart disease. These methods show promise for population-based studies of the anthropometric, environmental and genetic determinants of LV form and function in health and disease.Open Acces

Development and Application of Chemometric Methods for Modelling Metabolic Spectral Profiles

The interpretation of metabolic information is crucial to understanding the functioning of a biological system. Latent information about the metabolic state of a sample can be acquired using analytical chemistry methods, which generate spectroscopic profiles. Thus, nuclear magnetic resonance spectroscopy and mass spectrometry techniques can be employed to generate vast amounts of highly complex data on the metabolic content of biofluids and tissue, and this thesis discusses ways to process, analyse and interpret these data successfully. The evaluation of J -resolved spectroscopy in magnetic resonance profiling and the statistical techniques required to extract maximum information from the projections of these spectra are studied. In particular, data processing is evaluated, and correlation and regression methods are investigated with respect to enhanced model interpretation and biomarker identification. Additionally, it is shown that non-linearities in metabonomic data can be effectively modelled with kernel-based orthogonal partial least squares, for which an automated optimisation of the kernel parameter with nested cross-validation is implemented. The interpretation of orthogonal variation and predictive ability enabled by this approach are demonstrated in regression and classification models for applications in toxicology and parasitology. Finally, the vast amount of data generated with mass spectrometry imaging is investigated in terms of data processing, and the benefits of applying multivariate techniques to these data are illustrated, especially in terms of interpretation and visualisation using colour-coding of images. The advantages of methods such as principal component analysis, self-organising maps and manifold learning over univariate analysis are highlighted. This body of work therefore demonstrates new means of increasing the amount of biochemical information that can be obtained from a given set of samples in biological applications using spectral profiling. Various analytical and statistical methods are investigated and illustrated with applications drawn from diverse biomedical areas

Computational Tools for the Untargeted Assignment of FT-MS Metabolomics Datasets

Metabolomics is the study of metabolomes, the sets of metabolites observed in living systems. Metabolism interconverts these metabolites to provide the molecules and energy necessary for life processes. Many disease processes, including cancer, have a significant metabolic component that manifests as differences in what metabolites are present and in what quantities they are produced and utilized. Thus, using metabolomics, differences between metabolomes in disease and non-disease states can be detected and these differences improve our understanding of disease processes at the molecular level. Despite the potential benefits of metabolomics, the comprehensive investigation of metabolomes remains difficult. A popular analytical technique for metabolomics is mass spectrometry. Advances in Fourier transform mass spectrometry (FT-MS) instrumentation have yielded simultaneous improvements in mass resolution, mass accuracy, and detection sensitivity. In the metabolomics field, these advantages permit more complicated, but more informative experimental designs such as the use of multiple isotope-labeled precursors in stable isotope-resolved metabolomics (SIRM) experiments. However, despite these potential applications, several outstanding problems hamper the use of FT-MS for metabolomics studies. First, artifacts and data quality problems in FT-MS spectra can confound downstream data analyses, confuse machine learning models, and complicate the robust detection and assignment of metabolite features. Second, the assignment of observed spectral features to metabolites remains difficult. Existing targeted approaches for assignment often employ databases of known metabolites; however, metabolite databases are incomplete, thus limiting or biasing assignment results. Additionally, FT-MS provides limited structural information for observed metabolites, which complicates the determination of metabolite class (e.g. lipid, sugar, etc. ) for observed metabolite spectral features, a necessary step for many metabolomics experiments. To address these problems, a set of tools were developed. The first tool identifies artifacts with high peak density observed in many FT-MS spectra and removes them safely. Using this tool, two previously unreported types of high peak density artifact were identified in FT-MS spectra: fuzzy sites and partial ringing. Fuzzy sites were particularly problematic as they confused and reduced the accuracy of machine learning models trained on datasets containing these artifacts. Second, a tool called SMIRFE was developed to assign isotope-resolved molecular formulas to observed spectral features in an untargeted manner without a database of expected metabolites. This new untargeted method was validated on a gold-standard dataset containing both unlabeled and 15N-labeled compounds and was able to identify 18 of 18 expected spectral features. Third, a collection of machine learning models was constructed to predict if a molecular formula corresponds to one or more lipid categories. These models accurately predict the correct one of eight lipid categories on our training dataset of known lipid and non-lipid molecular formulas with precisions and accuracies over 90% for most categories. These models were used to predict lipid categories for untargeted SMIRFE-derived assignments in a non-small cell lung cancer dataset. Subsequent differential abundance analysis revealed a sub-population of non-small cell lung cancer samples with a significantly increased abundance in sterol lipids. This finding implies a possible therapeutic role of statins in the treatment and/or prevention of non-small cell lung cancer. Collectively these tools represent a pipeline for FT-MS metabolomics datasets that is compatible with isotope labeling experiments. With these tools, more robust and untargeted metabolic analyses of disease will be possible

Genetic mapping of metabolic biomarkers of cardiometabolic diseases

Cardiometabolic disorders (CMDs) are a major public health problem worldwide. The main goal of this thesis is to characterize the genetic architecture of CMD-related metabolites in a Lebanese cohort. In order to maximise the extraction of meaningful biological information from this dataset, an important part of this thesis focuses on the evaluation and subsequent improvement of the standard methods currently used for molecular epidemiology studies. First, I describe MetaboSignal, a novel network-based approach to explore the genetic regulation of the metabolome. Second, I comprehensively compare the recovery of metabolic information in the different 1H NMR strategies routinely used for metabolic profiling of plasma (standard 1D, spin-echo and JRES). Third, I describe a new method for dimensionality reduction of 1H NMR datasets prior to statistical modelling. Finally, I use all this methodological knowledge to search for molecular biomarkers of CMDs in a Lebanese population. Metabolome-wide association analyses identified a number of metabolites associated with CMDs, as well as several associations involving N-glycan units from acute-phase glycoproteins. Genetic mapping of these metabolites validated previously reported gene-metabolite associations, and revealed two novel loci associated with CMD-related metabolites. Collectively, this work contributes to the ongoing efforts to characterize the molecular mechanisms underlying complex human diseases.Open Acces

52nd Rocky Mountain Conference on Analytical Chemistry

Final program, abstracts, and information about the 52nd annual meeting of the Rocky Mountain Conference on Analytical Chemistry, co-endorsed by the Colorado Section of the American Chemical Society and the Society for Applied Spectroscopy. Held in Snowmass, Colorado, August 1-5, 2010

Practical Applications of NMR to Solve Real-World Problems

Nuclear magnetic resonance spectroscopy (NMR) has developed from primarily a method of academic study into a recognized technology that has advanced measurement capabilities within many different industrial sectors. These sectors include areas such as national security, energy, forensics, life sciences, pharmaceuticals, etc. Despite this diversity, these applications have many shared technical challenges and regulatory burdens, yet interdisciplinary cross-talk is often limited. To facilitate the sharing of knowledge, this Special Issue presents technical articles from four different areas, including the oil industry, nanostructured systems and materials, metabolomics, and biologics. These areas use NMR or magnetic resonance imaging (MRI) technologies that range from low-field relaxometry to magnetic fields as high as 700 MHz. Each article represents a practical application of NMR. A few articles are focused on basic research concepts, which will likely have the cross-cutting effect of advancing multiple disciplinary areas

A scanning ion conductance microscopy assay to investigate interactions between cell penetrating peptides and pore-suspending membranes

Die Rasterionenleitfähigkeitsmikroskopie (scanning ion conductance microscopy, SICM) stellt eine kontaktfreie Methode zur Ermittlung sowohl der Topographie als auch lokalen Ionenleitfähigkeit einer Oberfläche dar. Besonders vorteilhaft ist die Vermeidung mechanischer Beeinflussung bei der Untersuchung flexibler Strukturen, z.B. Lipiddoppelschichten wie Zellen oder künstlich erzeugter Lipidmembranen. Porenüberspannende Membranen (pore-suspending membranes, PSMs) verbinden als ein Beispiel für Modellsysteme eine hohe Stabilität mit lateraler Mobilität und dem Vorhandensein wässriger Kompartimente ober- und unterhalb der Doppelschicht, wie sie auch in der Natur gefunden werden. Ein wichtiges Forschungsgebiet stellt die Untersuchung der Wechselwirkung von Peptiden, besonders zellpenetrierenden Peptiden (cell penetrating peptides, CPPs), mit Lipiden und anderen Membranbestandteilen dar. Häufig untersuchte Beispiele sind Melittin, Hauptbestandteil des Giftes der Honigbiene Apis mellifera, sowie Penetratin, dritte Helix der Antennapedia Homöodomäne von Drosophila melanogaster. Generalisierte Protokolle zur Herstellung lösungsmittelfreier PSMs werden vorgestellt. Riesige unilamellare Vesikel (giant unilamellar vesicles, GUVs) unterschiedlicher Lipidzusammensetzung wurden hierzu auf porösem Siliziumnitrid (Si3N4), welches mit Cholesterylpolyethylenoxythiol (CPEO3, hydrophob) bzw. Mercaptoethanol (ME, hydrophil) funktionalisiert worden war, gespreitet. Verwendet wurden GUVs aus reinen Phosphatidylcholin (PC)-Lipiden sowie aus Mischungen von PC-Lipiden mit Cholesterol und PC-Lipiden mit Phosphatidylserin (PS)-Lipiden. Der Erfolg des Spreitvorgangs wurde durch Abbilden mittels konfokaler Rasterlasermikroskopie (confocal laser scanning microscopy, CLSM) und SICM verifiziert. Der Hauptteil dieser Arbeit behandelte die Entwicklung und Anwendung CLSM- und SICM-basierter CPP-Titrationsassays zur Aufklärung des Einflusses der Substratfunktionalisierung und der Lipidzusammensetzung der Membranen auf die Wechselwirkung zwischen Melittin bzw. Penetratin und den Lipiddoppelschichten. CLSM-Experimente wurden mit Melittin auf allen zur Verfügung stehenden PSMs sowohl auf hydrophob als auch hydrophil funktiona-lisierten Substraten durchgeführt, während Penetratin auf den drei unterschiedlichen PSMs auf hydrophil funktionalisierten Substraten verwendet wurde. Ein Reißen der Membranen wurde im Fall hydrophil funktionalisierter Substrate für beide Peptide im Bereich von 1–3 µM beobachtet. Bei hydrophob funktionalisierten Substraten induzierte eine dreifach geringere Melittinkonzentration die Zerstörung der Membranen. Sowohl auf hydrophob als auch auf hydrophil funktionalisierten Substraten wurde bei einem Cholesterolanteil von 10% eine Erhöhung der zum Reißen notwendigen Melittinkonzentratin erhalten, während bei 20% PS-Anteil eine Verschiebung zu geringeren Konzentrationen evident wurde. SICM-Experimente wurden mit Melittin auf PC/Cholesterol-PSMs auf hydrophob und hydrophil funktionalisierten Substraten und mit reinen PC-PSMs auf hydrophil funktionalisierten Membranen durchgeführt. Es wurden keine signifikanten Konzentrationsunterschiede beobachtet; die gefundenen Konzentrationsbereiche jedoch stimmten mit denen der CLSM-Experimente überein. Darüberhinaus wurde vor dem Reißen der Membranen ein Ansteigen der Porentiefe gefunden, das mit einer erhöhten Membranpermeabilität korrespondiert