An assessment of novel biodegradable magnesium alloys for endovascular biomaterial applications

Magnesium alloys have been widely explored as potential biomaterials, but several limitations to using these materials have prevented their widespread use, such as uncontrollable degradation kinetics which alter their mechanical properties. In an attempt to further the applicability of magnesium and its alloys for biomedical purposes, two novel magnesium alloys Mg-Zn-Cu and Mg-Zn-Se were developed with the expectation of improving upon the unfavorable qualities shown by similar magnesium based materials that have previously been explored. The overall performance of these novel magnesium alloys has been assessesed in three distinct phases of research: 1) analysing the mechanical properties of the as-cast magnesium alloys, 2) evaluating the biocompatibility of the as-cast magnesium alloys through the use of in-vitro cellular studies, and 3) profiling the degradation kinetics of the as-cast magnesium alloys through the use of electrochemical potentiodynamic polarization techqnique as well as gravimetric weight-loss methods. As compared to currently available shape memory alloys and degradable as-cast alloys, these experimental alloys possess superior as-cast mechanical properties with elongation at failure values of 12% and 13% for the Mg-Zn-Se and Mg-Zn-Se alloys, respectively. This is substantially higher than other as-cast magnesium alloys that have elongation at failure values that range from 7-10%. Biocompatibility tests revealed that both the Mg-Zn-Se and Mg-Zn-Cu alloys exhibit low cytotoxicity levels which are suitable for biomaterial applications. Gravimetric and electrochemical testing was indicative of the weight loss and initial corrosion behavior of the alloys once immersed within a simulated body fluid. The development of these novel as-cast magnesium alloys provide an advancement to the field of degradable metallic materials, while experimental results indicate their potential as cost-effective medical devices.^

A Research Platform for Artificial Neural Networks with Applications in Pediatric Epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and non-epileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that 1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and 2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).

Contributions to HEVC Prediction for Medical Image Compression

Medical imaging technology and applications are continuously evolving, dealing with images of increasing spatial and temporal resolutions, which allow easier and more accurate medical diagnosis. However, this increase in resolution demands a growing amount of data to be stored and transmitted. Despite the high coding efficiency achieved by the most recent image and video coding standards in lossy compression, they are not well suited for quality-critical medical image compression where either near-lossless or lossless coding is required. In this dissertation, two different approaches to improve lossless coding of volumetric medical images, such as Magnetic Resonance and Computed Tomography, were studied and implemented using the latest standard High Efficiency Video Encoder (HEVC). In a first approach, the use of geometric transformations to perform inter-slice prediction was investigated. For the second approach, a pixel-wise prediction technique, based on Least-Squares prediction, that exploits inter-slice redundancy was proposed to extend the current HEVC lossless tools. Experimental results show a bitrate reduction between 45% and 49%, when compared with DICOM recommended encoders, and 13.7% when compared with standard HEVC.

Development of Swept Source Optical Coherence Tomography and Adaptive Optics Scanning Laser Ophthalmoscopy: Improved Imaging Speed and Handheld Applications

Optical coherence tomography (OCT) is a noninvasive three-dimensional interferometric imaging technique capable of achieving micrometer scale resolution. It is now a standard of care in ophthalmology, where it is used to improve the accuracy of early diagnosis, to better understand the source of pathophysiology, and to monitor disease progression and response to therapy. In particular, retinal imaging has been the most prevalent clinical application of OCT, but researchers and companies alike are developing OCT systems for cardiology, dermatology, dentistry, and many other medical and industrial applications.

Adaptive optics (AO) is a technique used to reduce monochromatic aberrations in optical instruments. It is used in astronomical telescopes, laser communications, high-power lasers, retinal imaging, optical fabrication and microscopy to improve system performance. Scanning laser ophthalmoscopy (SLO) is a noninvasive confocal imaging technique that produces high contrast two-dimensional retinal images. AO is combined with SLO (AOSLO) to compensate for the wavefront distortions caused by the optics of the eye, providing the ability to visualize the living retina with cellular resolution. AOSLO has shown great promise to advance the understanding of the etiology of retinal diseases on a cellular level.

Broadly, we endeavor to enhance the vision outcome of ophthalmic patients through improved diagnostics and personalized therapy. Toward this end, the objective of the work presented herein was the development of advanced techniques for increasing the imaging speed, reducing the form factor, and broadening the versatility of OCT and AOSLO. Despite our focus on applications in ophthalmology, the techniques developed could be applied to other medical and industrial applications. In this dissertation, a technique to quadruple the imaging speed of OCT was developed. This technique was demonstrated by imaging the retinas of healthy human subjects. A handheld, dual depth OCT system was developed. This system enabled sequential imaging of the anterior segment and retina of human eyes. Finally, handheld SLO/OCT systems were developed, culminating in the design of a handheld AOSLO system. This system has the potential to provide cellular level imaging of the human retina, resolving even the most densely packed foveal cones.

Advanced Applications of 3D Dosimetry and 3D Printing in Radiation Therapy

As complex radiotherapy techniques become more readily-practiced, comprehensive 3D dosimetry is a growing necessity for advanced quality assurance. However, clinical implementation has been impeded by a wide variety of factors, including the expense of dedicated optical dosimeter readout tools, high operational costs, and the overall difficulty of use. To address these issues, a novel dry-tank optical CT scanner was designed for PRESAGE 3D dosimeter readout, relying on 3D printed components and omitting costly parts from preceding optical scanners. This work details the design, prototyping, and basic commissioning of the Duke Integrated-lens Optical Scanner (DIOS).

The convex scanning geometry was designed in ScanSim, an in-house Monte Carlo optical ray-tracing simulation. ScanSim parameters were used to build a 3D rendering of a convex ‘solid tank’ for optical-CT, which is capable of collimating a point light source into telecentric geometry without significant quantities of refractive-index matched fluid. The model was 3D printed, processed, and converted into a negative mold via rubber casting to produce a transparent polyurethane scanning tank. The DIOS was assembled with the solid tank, a 3W red LED light source, a computer-controlled rotation stage, and a 12-bit CCD camera. Initial optical phantom studies show negligible spatial inaccuracies in 2D projection images and 3D tomographic reconstructions. A PRESAGE 3D dose measurement for a 4-field box treatment plan from Eclipse shows 95% of voxels passing gamma analysis at 3%/3mm criteria. Gamma analysis between tomographic images of the same dosimeter in the DIOS and DLOS systems show 93.1% agreement at 5%/1mm criteria. From this initial study, the DIOS has demonstrated promise as an economically-viable optical-CT scanner. However, further improvements will be necessary to fully develop this system into an accurate and reliable tool for advanced QA.

Pre-clinical animal studies are used as a conventional means of translational research, as a midpoint between in-vitro cell studies and clinical implementation. However, modern small animal radiotherapy platforms are primitive in comparison with conventional linear accelerators. This work also investigates a series of 3D printed tools to expand the treatment capabilities of the X-RAD 225Cx orthovoltage irradiator, and applies them to a feasibility study of hippocampal avoidance in rodent whole-brain radiotherapy.

As an alternative material to lead, a novel 3D-printable tungsten-composite ABS plastic, GMASS, was tested to create precisely-shaped blocks. Film studies show virtually all primary radiation at 225 kVp can be attenuated by GMASS blocks of 0.5cm thickness. A state-of-the-art software, BlockGen, was used to create custom hippocampus-shaped blocks from medical image data, for any possible axial treatment field arrangement. A custom 3D printed bite block was developed to immobilize and position a supine rat for optimal hippocampal conformity. An immobilized rat CT with digitally-inserted blocks was imported into the SmART-Plan Monte-Carlo simulation software to determine the optimal beam arrangement. Protocols with 4 and 7 equally-spaced fields were considered as viable treatment options, featuring improved hippocampal conformity and whole-brain coverage when compared to prior lateral-opposed protocols. Custom rodent-morphic PRESAGE dosimeters were developed to accurately reflect these treatment scenarios, and a 3D dosimetry study was performed to confirm the SmART-Plan simulations. Measured doses indicate significant hippocampal sparing and moderate whole-brain coverage.

A Framework to Support the Sharing and Reuse of Computable Phenotype Definitions Across Health Care Delivery and Clinical Research Applications.

INTRODUCTION: The ability to reproducibly identify clinically equivalent patient populations is critical to the vision of learning health care systems that implement and evaluate evidence-based treatments. The use of common or semantically equivalent phenotype definitions across research and health care use cases will support this aim. Currently, there is no single consolidated repository for computable phenotype definitions, making it difficult to find all definitions that already exist, and also hindering the sharing of definitions between user groups. METHOD: Drawing from our experience in an academic medical center that supports a number of multisite research projects and quality improvement studies, we articulate a framework that will support the sharing of phenotype definitions across research and health care use cases, and highlight gaps and areas that need attention and collaborative solutions. FRAMEWORK: An infrastructure for re-using computable phenotype definitions and sharing experience across health care delivery and clinical research applications includes: access to a collection of existing phenotype definitions, information to evaluate their appropriateness for particular applications, a knowledge base of implementation guidance, supporting tools that are user-friendly and intuitive, and a willingness to use them. NEXT STEPS: We encourage prospective researchers and health administrators to re-use existing EHR-based condition definitions where appropriate and share their results with others to support a national culture of learning health care. There are a number of federally funded resources to support these activities, and research sponsors should encourage their use.

Laser-Driven Ion Accelerators: State of the Art and Applications

Laser-plasma based accelerators of protons and heavier ions are a source of potential interest for several applications, including in the biomedical area. While the potential future use in cancer hadrontherapy acts as a strong aspirational motivation for this research field, radiobiology employing laser-driven ion bursts is alreadyan active field of research. Here we give a summary of the state of the art in laser driven ion acceleration, of the main challenges currently faced by the research inthis field and of some of the current and future strategies for overcoming them.

Proposing the deep dynamic Bayesian network as a future computer based medical system

The development of new learning models has been of great importance throughout recent years, with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.

Studies on betanin natural dye incorporated ZnO composites at nano and micro scale for photonic device applications

Green energy and Green technology are the most of the quoted terms in the context of modern science and technology. Technology which is close to nature is the necessity of the modern world which is haunted by global warming and climatic alterations. Proper utilization of solar energy is one of the goals of Green Energy Movement. The present thesis deals with the work carried out in the eld of nanotechnology and its possible use in various applications (employing natural dyes) like solar cells. Unlike arti cial dyes, the natural dyes are available, easy to prepare, low in cost, non-toxic, environmentally friendly and fully biodegradable. Looking to the 21st century, the nano/micro sciences will be a chief contributor to scienti c and technological developments. As nanotechnology progresses and complex nanosystems are fabricated, a growing impetus is being given to the development of multi-functional and size-dependent materials. The control of the morphology, from the nano to the micrometer scales, associated with the incorporation of several functionalities can yield entirely new smart hybrid materials. They are special class of materials which provide a new method for the improvement of the environmental stability of the material with interesting optical properties and opening a land of opportunities for applications in the eld of photonics. Zinc oxide (ZnO) is one such multipurpose material that has been explored for applications in sensing, environmental monitoring, and bio-medical systems and communications technology. Understanding the growth mechanism and tailoring their morphology is essential for the use of ZnO crystals as nano/micro electromechanical systems and also as building blocks of other nanosystems.

Recouvrements à base de dextrane pour applications médicales

L’ingénierie des biomatériaux a connu un essor prodigieux ces dernières décennies passant de matériaux simples à des structures plus complexes, particulièrement dans le domaine cardiovasculaire. Cette évolution découle de la nécessité des biomatériaux de permettre la synergie de différentes propriétés, dépendantes de leurs fonctions, qui ne sont pas forcément toutes compatibles. Historiquement, les premiers matériaux utilisés dans la conception de dispositifs médicaux étaient ceux présentant le meilleur compromis entre les propriétés physico-chimiques, mécaniques et biologiques que nécessitait leur application. Cependant, il se peut qu’un tel dispositif possède les bonnes propriétés physico-chimiques ou mécaniques, mais que sa biocompatibilité soit insuffisante induisant ainsi des complications cliniques. Afin d’améliorer ces propriétés biologiques tout en conservant les propriétés de volume du matériau, une solution est d’en modifier la surface. L’utilisation d’un revêtement permet alors de moduler la réponse biologique à l’interface biomatériau-hôte et de diminuer les effets indésirables. Ces revêtements sont optimisés selon deux critères principaux : la réponse biologique et la réponse mécanique. Pour la réponse biologique, les deux approches principales sont de mettre au point des revêtements proactifs qui engendrent l’adhérence, la prolifération ou la migration cellulaire, ou passifs, qui, principalement, sont inertes et empêchent l’adhérence de composés biologiques. Dans certains cas, il est intéressant de pouvoir favoriser certaines cellules et d’en limiter d’autres, par exemple pour lutter contre la resténose, principalement due à la prolifération incontrôlée de cellules musculaires lisses qui conduit à une nouvelle obstruction de l’artère, suite à la pose d’un stent. La recherche sur les revêtements de stents vise, alors, à limiter la prolifération de ces cellules tout en facilitant la ré-endothélialisation, c’est-à-dire en permettant l’adhérence et la prolifération de cellules endothéliales. Dans d’autres cas, il est intéressant d’obtenir des surfaces limitant toute adhérence cellulaire, comme pour l’utilisation de cathéter. Selon leur fonction, les cathéters doivent empêcher l’adhérence cellulaire, en particulier celle des bactéries provoquant des infections, et être hémocompatibles, principalement dans le domaine vasculaire. Il a été démontré lors d’études précédentes qu’un copolymère à base de dextrane et de poly(méthacrylate de butyle) (PBMA) répondait aux problématiques liées à la resténose et qu’il possédait, de plus, une bonne élasticité, propriété mécanique importante due à la déformation que subit le stent lors de son déploiement. L’approche de ce projet était d’utiliser ce copolymère comme revêtement de stents et d’en améliorer l’adhérence à la surface en formant des liens covalents avec la surface. Pour ce faire, cela nécessitait l’activation de la partie dextrane du copolymère afin de pouvoir le greffer à la surface aminée. Il était important de vérifier pour chaque étape l’influence des modifications effectuées sur les propriétés biologiques et mécaniques des matériaux obtenus, mais aussi d’un point de vue de la chimie, l’influence que cette modification pouvait induire sur la réaction de copolymérisation. Dans un premier temps, seul le dextrane est considéré et est modifié par oxydation et carboxyméthylation puis greffé à des surfaces fluorocarbonées aminées. L’analyse physico-chimique des polymères de dextrane modifiés et de leur greffage permet de choisir une voie de modification préférentielle qui n’empêchera pas ultérieurement la copolymérisation. La carboxyméthylation permet ainsi d’obtenir un meilleur recouvrement de la surface tout en conservant la structure polysaccharidique du dextrane. Le greffage du dextrane carboxyméthylé (CMD) est ensuite optimisé selon différents degrés de modification, tenant compte aussi de l’influence que ces modifications peuvent induire sur les propriétés biologiques. Finalement, les CMD précédemment étudiés, avec des propriétés biologiques définies, sont copolymérisés avec des monomères de méthacrylate de butyle (BMA). Les copolymères ainsi obtenus ont été ensuite caractérisés par des analyses physico-chimiques, biologiques et mécaniques. Des essais préliminaires ont montrés que les films de copolymères étaient anti-adhérents vis-à-vis des cellules, ce qui a permis de trouver de nouvelles applications au projet. Les propriétés élastiques et anti-adhérentes présentées par les films de copolymères CMD-co-PBMA, les rendent particulièrement intéressants pour des applications comme revêtements de cathéters.

Improving the Accuracy of 3-D Reconstruction in Robotic Vision Applications

Thesis (Ph.D.)--University of Washington, 2016-08

CAUSAL INFERENCE WITH A CONTINUOUS TREATMENT AND OUTCOME: ALTERNATIVE ESTIMATORS FOR PARAMETRIC DOSE-RESPONSE FUNCTIONS WITH APPLICATIONS.

Causal inference with a continuous treatment is a relatively under-explored problem. In this dissertation, we adopt the potential outcomes framework. Potential outcomes are responses that would be seen for a unit under all possible treatments. In an observational study where the treatment is continuous, the potential outcomes are an uncountably infinite set indexed by treatment dose. We parameterize this unobservable set as a linear combination of a finite number of basis functions whose coefficients vary across units. This leads to new techniques for estimating the population average dose-response function (ADRF). Some techniques require a model for the treatment assignment given covariates, some require a model for predicting the potential outcomes from covariates, and some require both. We develop these techniques using a framework of estimating functions, compare them to existing methods for continuous treatments, and simulate their performance in a population where the ADRF is linear and the models for the treatment and/or outcomes may be misspecified. We also extend the comparisons to a data set of lottery winners in Massachusetts. Next, we describe the methods and functions in the R package causaldrf using data from the National Medical Expenditure Survey (NMES) and Infant Health and Development Program (IHDP) as examples. Additionally, we analyze the National Growth and Health Study (NGHS) data set and deal with the issue of missing data. Lastly, we discuss future research goals and possible extensions.

Study and development of UV photodetectors envisaging COMPASS RICH-1 upgrade and applications

COMPASS is an experiment at CERN’s SPS whose goal is to study hadron structure and spectroscopy. The experiment includes a wide acceptance RICH detector, operating since 2001 and subject to a major upgrade of the central region of its photodetectors in 2006. The remaining 75% of the photodetection area are still using MWPCs from the original design, who suffer from limitations in gain due to aging of the photocathodes from ion bombardment and due to ion-induced instabilities. Besides the mentioned limitations, the increased luminosity conditions expected for the upcoming years of the experiment make an upgrade to the remaining detectors pertinent. This upgrade should be accomplished in 2016, using hybrid detectors composed of ThGEMs and MICROMEGAS. This work presents the study, development and characterization of gaseous photon detectors envisaging the foreseen upgrade, and the progress in production and evaluation techniques necessary to reach increasingly larger area detectors with the performances required. It includes reports on the studies performed under particle beam environment of such detectors. MPGD structures can also be used in a variety of other applications, of which nuclear medical imaging is a notorious example. This work includes, additionally, the initial steps in simulating, assembling and characterizing a prototype of a gaseous detector for application as a Compton Camera.

Tensor Completion for Multidimensional Inverse Problems with Applications to Magnetic Resonance Relaxometry

This thesis deals with tensor completion for the solution of multidimensional inverse problems. We study the problem of reconstructing an approximately low rank tensor from a small number of noisy linear measurements. New recovery guarantees, numerical algorithms, non-uniform sampling strategies, and parameter selection algorithms are developed. We derive a fixed point continuation algorithm for tensor completion and prove its convergence. A restricted isometry property (RIP) based tensor recovery guarantee is proved. Probabilistic recovery guarantees are obtained for sub-Gaussian measurement operators and for measurements obtained by non-uniform sampling from a Parseval tight frame. We show how tensor completion can be used to solve multidimensional inverse problems arising in NMR relaxometry. Algorithms are developed for regularization parameter selection, including accelerated k-fold cross-validation and generalized cross-validation. These methods are validated on experimental and simulated data. We also derive condition number estimates for nonnegative least squares problems. Tensor recovery promises to significantly accelerate N-dimensional NMR relaxometry and related experiments, enabling previously impractical experiments. Our methods could also be applied to other inverse problems arising in machine learning, image processing, signal processing, computer vision, and other fields.

Fibre components and systems for mid-IR applications

Fibre-optic components and systems are used in a wide variety of industrial, medical and communication applications and can be found in use everywhere in the modern world, from the bottom of the ocean to satellites in orbit. The field of fibre optics has seen rapid growth in the past few decades to become an essential enabling technology. However, much more work is needed to develop components and systems that can work at wavelengths in the short-wavelength infrared (SWIR) / mid-IR part of the spectrum (defined in this work as 1.5 – 4.5.