Superresolution Reconstruction for Magnetic Resonance Spectroscopic Imaging Exploiting Low-Rank Spatio-Spectral Structure

Magnetic resonance spectroscopic imaging (MRSI) is a rapidly developing medical imaging modality, capable of conferring both spatial and spectral information content, and has become a powerful clinical tool. The ability to non-invasively observe spatial maps of metabolite concentrations, for instance, in the human brain, can offer functional, as well as pathological insights, perhaps even before structural aberrations or behavioral symptoms are evinced. Despite its lofty clinical prospects, MRSI has traditionally remained encumbered by a number of practical limitations. Of primary concern are the vastly reduced concentrations of tissue metabolites when compared to that of water, which forms the basis for conventional MR imaging. Moreover, the protracted exam durations required by MRSI routinely approach the limits for patient compliance. Taken in conjunction, the above considerations effectively circumscribe the data collection process, ultimately translating to coarse image resolutions that are of diminished clinical utility. Such shortcomings are compounded by spectral contamination artifacts due to the system pointspread function, which arise as a natural consequence when reconstructing non-band-limited data by the inverse Fourier transform. These artifacts are especially pronounced near regions characterized by substantial discrepancies in signal intensity, for example, the interface between normal brain and adipose tissue, whereby the metabolite signals are inundated by the dominant lipid resonances. In recent years, concerted efforts have been made to develop alternative, non-Fourier MRSI reconstruction strategies that aim to surmount the aforementioned limitations. In this dissertation, we build upon the burgeoning medley of innovative and promising techniques, proffering a novel superresolution reconstruction framework predicated on the recent interest in low-rank signal modeling, along with state-of-the-art regularization methods. The proposed framework is founded upon a number of key tenets. Firstly, we proclaim that the underlying spatio-spectral distribution of the investigated object admits a bilinear representation, whereby spatial and spectral signal components can be effectively segregated. We further maintain that the dimensionality of the subspace spanned by the components is, in principle, bounded by a modest number of observable metabolites. Secondly, we assume that local susceptibility effects represent the primary sources of signal corruption that tend to disallow such representations. Finally, we assert that the spatial components belong to a class of real-valued, non-negative, and piecewise linear functions, compelled in part through the use of a total variation regularization penalty. After demonstrating superior spatial and spectral localization properties in both numerical and physical phantom data when compared against standard Fourier methods, we proceed to evaluate reconstruction performance in typical in vivo settings, whereby the method is extended in order to promote the recovery of signal variations throughout the MRSI slice thickness. Aside from the various technical obstacles, one of the cardinal prospective challenges for high-resolution MRSI reconstruction is the shortfall of reliable ground truth data prudent for validation, thereby prompting reservations surrounding the resulting experimental outcomes. [...

Quantitative Assessment of Magnetic Resonance Spectroscopy Data Reconstruction Methods: Region-of-Interest Averaging and Spectral Localization by Imaging

The aim of this dissertation was to compare two magnetic resonance spectroscopy (MRS) localization techniques: Fourier based region-of-interest (ROI) averaging, and the non-Fourier based spectral localization by imaging (SLIM). Unlike ROI-averaging, SLIM provides a technique for calculating the metabolite spectra of a compartmental region without the need for averaging voxels of spectral data to estimate that region. Because of this, SLIM has the potential to greatly reduce the acquisition time needed to acquire compartmental spectra. SLIM was processed over multiple k-space sizes and over an assortment of brain regions and then these results were compared to their equivalent ROI-averaged regions. The assorted k-space sizes were used to demonstrate SLIM operating with different amounts of available data, which was used to compare the process to ROI-averaging. The results of this study validate SLIM as a valuable localization tool that will shorten scan times and improve accuracy in spectral localization. The dissertation is divided into five main chapters: (1) Introduction, which addresses magnetic resonance background concepts and applications of MRS techniques; (2) Methods, which describes the processes involved in developing a programming pipeline designed to produce metabolite data for the localization techniques; (3) Results, which provides statistical measures of the localization methods; (4) Discussion, where comparisons were drawn from the datasets based on the results section; (5) Conclusions, which evaluates the thesis work and addresses possible research directions for the future

ADVANCED INTRAVASCULAR MAGNETIC RESONANCE IMAGING WITH INTERACTION

Intravascular (IV) Magnetic Resonance Imaging (MRI) is a specialized class of interventional MRI (iMRI) techniques that acquire MRI images through blood vessels to guide, identify and/or treat pathologies inside the human body which are otherwise difficult to locate and treat precisely. Here, interactions based on real-time computations and feedback are explored to improve the accuracy and efficiency of IVMRI procedures. First, an IV MRI-guided high-intensity focused ultrasound (HIFU) ablation method is developed for targeting perivascular pathology with minimal injury to the vessel wall. To take advantage of real-time feedback, a software interface is developed for monitoring thermal dose with real-time MRI thermometry, and an MRI-guided ablation protocol developed and tested on muscle and liver tissue ex vivo. It is shown that, with cumulative thermal dose monitored with MRI thermometry, lesion location and dimensions can be estimated consistently, and desirable thermal lesions can be achieved in animals in vivo. Second, to achieve fully interactive IV MRI, high-resolution real-time 10 frames-per-second (fps) MRI endoscopy is developed as an advance over prior methods of MRI endoscopy. Intravascular transmit-receive MRI endoscopes are fabricated for highly under-sampled radial-projection MRI in a clinical 3Tesla MRI scanner. Iterative nonlinear reconstruction is accelerated using graphics processor units (GPU) to achieve true real-time endoscopy visualization at the scanner. The results of high-speed MRI endoscopy at 6-10 fps are consistent with fully-sampled MRI endoscopy and histology, with feasibility demonstrated in vivo in a large animal model. Last, a general framework for automatic imaging contrast tuning over MRI protocol parameters is explored. The framework reveals typical signal patterns over different protocol parameters from calibration imaging data and applies this knowledge to design efficient acquisition strategies and predicts contrasts under unacquired protocols. An external computer in real-time communication with the MRI console is utilized for online processing and controlling MRI acquisitions. This workflow enables machine learning for optimizing acquisition strategies in general, and provides a foundation for efficiently tuning MRI protocol parameters to perform interventional MRI in the highly varying and interactive environments commonly in play. This work is loosely inspired by prior research on extremely accelerated MRI relaxometry using the minimal-acquisition linear algebraic modeling (SLAM) method

Cerebral Hemodynamics in High-Risk Neonates Probed by Diffuse Optical Spectroscopies

Advances in medical and surgical care of the critically ill neonates have decreasedmortality, yet a significant number of these neonates suffer from neurodevelopmentaldelays and failure in school. Thus, clinicians are now focusing on prevention ofneurologic injury and improvement of neurocognitive outcome in these high-risk infants. Assessment of cerebral oxygenation, cerebral blood volume, and the regulation of cerebral blood flow (CBF) during the neonatal period is vital for evaluating brain health. Traditional CBF imaging methods fail, however, for both ethical and logistical reasons. In this dissertation, I demonstrate the use of non-invasive optical modalities, i.e., diffuse optical spectroscopy and diffuse correlation spectroscopy, to study cerebral oxygenation and cerebral blood flow in the critically ill neonatal population. The optical techniques utilize near-infrared (NIR) light to probe the static and dynamic physiological properties of deep tissues. Diffuse correlation spectroscopy (DCS) employs the transport of temporal correlation functions of diffusing light to extract relative changes in blood flow in biological tissues. Diffuse optical spectroscopy (DOS) employs the wavelength-dependent attenuation of NIR light to assess the concentrations of the primary chromophores in the tissue, namely oxy- and deoxy-hemoglobin. This dissertation presents both validation and clinical applications of novel diffuse optical spectroscopies in two specific critically ill neonatal populations: very-low birth weight preterm infants,and infants born with complex congenital heart defects. For validation of DCS in neonates, the blood flow index quantified by DCS is shown to correlate well with velocity measurements in the middle cerebral artery acquired by transcranial Doppler ultrasound. In patients with congenital heart defects DCS-measured relative changes in CBF due to hypercapnia agree strongly with relative changes in blood flow in the jugular veins as measured by phase-encoded velocity mapping magnetic resonance. For applications in the clinic, CO2 reactivity in patients with congenital heart defects prior to various stages of reconstructive surgery was quantified; our initial results suggest that CO2 reactivity is not systematically related to brain injury in this population. Additionally, the cerebral effects of various interventions, such as blood transfusion and sodium bicarbonate infusion, were investigated. In preterm infants, monitoring with DCS reveals a resilience of these patients to maintain constant CBF during a small postural manipulation

13th International Bologna Conference on Magnetic Resonance in Porous Media - Bologna 2016 Conference Handbook and Book of Abstracts

This conference series, founded at the University of Bologna in 1990 and now at the 13th edition, is devoted to the progress in Magnetic Resonance in Porous Media and in our understanding of Porous Media themselves, and to stimulate the contact among people from various parts of Academia and Industry. Researchers in Physics, Chemistry, Engineering, Life Sciences, Mathematics, Computer Sciences, and in Industrial Applications will benefit from exchange of ideas, experiences, and new approaches. Topics will include innovative techniques to study structure, behavior of fluids, and their interactions in every kind of natural and artificial porous materials, including rocks, cements, biological tissues, foodstuffs, wood, particle packs, sediments, pharmaceuticals, zeolites, and bioconstructs. New data acquisition and processing techniques are also expected to be strong features

13th International Bologna Conference on Magnetic Resonance in Porous Media - Bologna 2016 Conference Handbook and Book of Abstracts

This conference series, founded at the University of Bologna in 1990 and now at the 13th edition, is devoted to the progress in Magnetic Resonance in Porous Media and in our understanding of Porous Media themselves, and to stimulate the contact among people from various parts of Academia and Industry. Researchers in Physics, Chemistry, Engineering, Life Sciences, Mathematics, Computer Sciences, and in Industrial Applications will benefit from exchange of ideas, experiences, and new approaches. Topics will include innovative techniques to study structure, behavior of fluids, and their interactions in every kind of natural and artificial porous materials, including rocks, cements, biological tissues, foodstuffs, wood, particle packs, sediments, pharmaceuticals, zeolites, and bioconstructs. New data acquisition and processing techniques are also expected to be strong features

Semantic Segmentation of Ambiguous Images

Medizinische Bilder können schwer zu interpretieren sein. Nicht nur weil das Erkennen von Strukturen und möglichen Veränderungen Erfahrung und jahrelanges Training bedarf, sondern auch weil die dargestellten Messungen oft im Kern mehrdeutig sind. Fundamental ist dies eine Konsequenz dessen, dass medizinische Bild-Modalitäten, wie bespielsweise MRT oder CT, nur indirekte Messungen der zu Grunde liegenden molekularen Identitäten bereithalten. Die semantische Bedeutung eines Bildes kann deshalb im Allgemeinen nur gegeben einem größeren Bild-Kontext erfasst werden, welcher es oft allerdings nur unzureichend erlaubt eine eindeutige Interpretation in Form einer einzelnen Hypothese vorzunehmen. Ähnliche Szenarien existieren in natürlichen Bildern, in welchen die Kontextinformation, die es braucht um Mehrdeutigkeiten aufzulösen, limitiert sein kann, beispielsweise aufgrund von Verdeckungen oder Rauschen in der Aufnahme. Zusätzlich können überlappende oder vage Klassen-Definitionen zu schlecht gestellten oder diversen Lösungsräumen führen. Die Präsenz solcher Mehrdeutigkeiten kann auch das Training und die Leistung von maschinellen Lernverfahren beeinträchtigen. Darüber hinaus sind aktuelle Modelle ueberwiegend unfähig komplex strukturierte und diverse Vorhersagen bereitzustellen und stattdessen dazu gezwungen sich auf sub-optimale, einzelne Lösungen oder ununterscheidbare Mixturen zu beschränken. Dies kann besonders problematisch sein wenn Klassifikationsverfahren zu pixel-weisen Vorhersagen wie in der semantischen Segmentierung skaliert werden. Die semantische Segmentierung befasst sich damit jedem Pixel in einem Bild eine Klassen-Kategorie zuzuweisen. Diese Art des detailierten Bild-Verständnisses spielt auch eine wichtige Rolle in der Diagnose und der Behandlung von Krankheiten wie Krebs: Tumore werden häufig in MRT oder CT Bildern entdeckt und deren präzise Lokalisierung und Segmentierung ist von grosser Bedeutung in deren Bewertung, der Vorbereitung möglicher Biopsien oder der Planung von Fokal-Therapien. Diese klinischen Bildverarbeitungen, aber auch die optische Wahrnehmung unserer Umgebung im Rahmen von täglichen Aufgaben wie dem Autofahren, werden momentan von Menschen durchgeführt. Als Teil des zunehmenden Einbindens von maschinellen Lernverfahren in unsere Entscheidungsfindungsprozesse, ist es wichtig diese Aufgaben adequat zu modellieren. Dies schliesst Unsicherheitsabschätzungen der Modellvorhersagen mit ein, mitunter solche Unsicherheiten die den Bild-Mehrdeutigkeiten zugeschrieben werden können. Die vorliegende Thesis schlägt mehrere Art und Weisen vor mit denen mit einer mehrdeutigen Bild-Evidenz umgegangen werden kann. Zunächst untersuchen wir den momentanen klinischen Standard der im Falle von Prostata Läsionen darin besteht, die MRT-sichtbaren Läsionen subjektiv auf ihre Aggressivität hin zu bewerten, was mit einer hohen Variabilität zwischen Bewertern einhergeht. Unseren Studien zufolge können bereits einfache machinelle Lernverfahren und sogar simple quantitative MRT-basierte Parameter besser abschneiden als ein individueller, subjektiver Experte, was ein vielversprechendes Potential der Quantifizerung des Prozesses nahelegt. Desweiteren stellen wir die derzeit erfolgreichste Segmentierungsarchitektur auf einem stark mehrdeutigen Datensatz zur Probe der während klinischer Routine erhoben und annotiert wurde. Unsere Experimente zeigen, dass die standard Segmentierungsverlustfuntion in Szenarien mit starkem Annotationsrauschen sub-optimal sein kann. Als eine Alternative erproben wir die Möglichkeit ein Modell der Verlustunktion zu lernen mit dem Ziel die Koexistenz von plausiblen Lösungen während des Trainings zuzulassen. Wir beobachten gesteigerte Performanz unter Verwendung dieser Trainingsmethode für ansonsten unveränderte neuronale Netzarchitekturen und finden weiter gesteigerte relative Verbesserungen im Limit weniger Daten. Mangel an Daten und Annotationen, hohe Maße an Bild- und Annotationsrauschen sowie mehrdeutige Bild-Evidenz finden sich besonders häufig in Datensätzen medizinischer Bilder wieder. Dieser Teil der Thesis exponiert daher einige der Schwächen die standard Techniken des maschinellen Lernens im Lichte dieser Besonderheiten aufweisen können. Derzeitige Segmentierungsmodelle, wie die zuvor Herangezogenen, sind dahingehend eingeschränkt, dass sie nur eine einzige Vorhersage abgeben können. Dies kontrastiert die Beobachtung dass eine Gruppe von Annotierern, gegeben mehrdeutiger Bilddaten, typischer Weise eine Menge an diverser aber plausibler Annotationen produziert. Um die vorgenannte Modell-Einschränkung zu beheben und die angemessen probabilistische Behandlung der Aufgabe zu ermöglichen, entwickeln wir zwei Modelle, die eine Verteilung über plausible Annotationen vorhersagen statt nur einer einzigen, deterministischen Annotation. Das erste der beiden Modelle kombiniert ein `encoder-decoder\u27 Modell mit dem Verfahren der `variational inference\u27 und verwendet einen globalen `latent vector\u27, der den Raum der möglichen Annotationen für ein gegebenes Bild kodiert. Wir zeigen, dass dieses Modell deutlich besser als die Referenzmethoden abschneidet und gut kalibrierte Unsicherheiten aufweist. Das zweite Modell verbessert diesen Ansatz indem es eine flexiblere und hierarchische Formulierung verwendet, die es erlaubt die Variabilität der Segmentierungen auf verschiedenden Skalen zu erfassen. Dies erhöht die Granularität der Segmentierungsdetails die das Modell produzieren kann und erlaubt es unabhängig variierende Bildregionen und Skalen zu modellieren. Beide dieser neuartigen generativen Segmentierungs-Modelle ermöglichen es, falls angebracht, diverse und kohärente Bild Segmentierungen zu erstellen, was im Kontrast zu früheren Arbeiten steht, welche entweder deterministisch sind, die Modellunsicherheiten auf der Pixelebene modellieren oder darunter leiden eine unangemessen geringe Diversität abzubilden. Im Ergebnis befasst sich die vorliegende Thesis mit der Anwendung von maschinellem Lernen für die Interpretation medizinischer Bilder: Wir zeigen die Möglichkeit auf den klinischen Standard mit Hilfe einer quantitativen Verwendung von Bildparametern, die momentan nur subjektiv in Diagnosen einfliessen, zu verbessern, wir zeigen den möglichen Nutzen eines neuen Trainingsverfahrens um die scheinbare Verletzlichkeit der standard Segmentierungsverlustfunktion gegenüber starkem Annotationsrauschen abzumildern und wir schlagen zwei neue probabilistische Segmentierungsmodelle vor, die die Verteilung über angemessene Annotationen akkurat erlernen können. Diese Beiträge können als Schritte hin zu einer quantitativeren, verstärkt Prinzipien-gestützten und unsicherheitsbewussten Analyse von medizinischen Bildern gesehen werden -ein wichtiges Ziel mit Blick auf die fortschreitende Integration von lernbasierten Systemen in klinischen Arbeitsabläufen

Cultivate Quantitative Magnetic Resonance Imaging Methods to Measure Markers of Health and Translate to Large Scale Cohort Studies

Magnetic Resonance Imaging (MRI) is an indispensable tool in healthcare and research, with a growing demand for its services. The appeal of MRI stems from its non-ionizing radiation nature, ability to generate high-resolution images of internal organs and structures without invasive procedures, and capacity to provide quantitative assessments of tissue properties such as ectopic fat, body composition, and organ volume. All without long term side effects. Nine published papers are submitted which show the cultivation of quantitative measures of ectopic fat within the liver and pancreas using MRI, and the process of validating whole-body composition and organ volume measurements. All these techniques have been translated into large-scale studies to improve health measurements in large population cohorts. Translating this work into large-scale studies, including the use of artificial intelligence, is included. Additionally, an evaluation accompanies these published studies, appraising the evolution of these quantitative MRI techniques from the conception to their application in large cohort studies. Finally, this appraisal provides a summary of future work on crowdsourcing of ground truth training data to facilitate its use in wider applications of artificial intelligence.In conclusion, this body of work presents a portfolio of evidence to fulfil the requirements of a PhD by published works at the University of Salford

Functional Gold Nanoparticles for Biomedical Applications

Abstract Subjects of the present dissertation are the synthesis, the functionalization and the characterization of colloidal gold nanoparticles. The employed nanoparticles consist of an inorganic Au core of approximately 5 nm diameter, which is stabilized by hydrophobic surface molecules. To transfer the nanoparticles to aqueous environments (an indispensable necessity for biomedical applications) they are coated with an amphiphilic polymer, which generates water solubility and moreover gives the ability for further functionalization. The physico-chemical properties of such nanoparticles are verified within different purposes: First, several fundamental intrinsic surface properties are quantified, including the establishment of pH titration as characterization tool. It is found that the carboxylic groups, responsible for the colloidal stabilization, partly have different properties (like their pKa) compared to free standing carboxylic acids. These findings are crucial for the colloidal stabilization of nanoparticles as well as for their further functionalization. Secondly, two species of fluorescently labeled nanoparticles, which differed in first order only in the net surface charge, are employed to study charge dependent interaction of nanoparticles with biological systems, including proteins as well as living cells. The main finding is, that a so called protein corona forms around nanoparticles, what has far-reaching impacts on cell internalization abilities. Moreover it is found that positively charged nanoparticles show a higher cell association as well as a higher toxicity. Thirdly, nanoparticles are modified towards sensing applications by surface functionalization with ion sensitive dyes. Positively charged nanoparticles are modified with a Cl- sensitive dye and negatively charged nanoparticles are modified with a Zn2+ sensitive dye. The goals of the dissertation can be synoptically depicted as: 1) Extension of the existing techniques for nanoparticle functionalization, particularly regarding new types of functional polymers. 2) A fundamental and comprehensive characterization of nanoparticles ranging from the verification of intrinsic, physico-chemical properties to biomedical applications