Enhancing the Visualization of the Peripheral Retina with Wide Field-of-View Optical Coherence Tomography

The goal of my Ph.D. thesis is to enhance the visualization of the peripheral retina using wide-field optical coherence tomography (OCT) in a clinical setting.

OCT has gain widespread adoption in clinical ophthalmology due to its ability to visualize the diseases of the macula and central retina in three-dimensions, however, clinical OCT has a limited field-of-view of 300. There has been increasing interest to obtain high-resolution images outside of this narrow field-of-view, because three-dimensional imaging of the peripheral retina may prove to be important in the early detection of neurodegenerative diseases, such as Alzheimer's and dementia, and the monitoring of known ocular diseases, such as diabetic retinopathy, retinal vein occlusions, and choroid masses.

Before attempting to build a wide-field OCT system, we need to better understand the peripheral optics of the human eye. Shack-Hartmann wavefront sensors are commonly used tools for measuring the optical imperfections of the eye, but their acquisition speed is limited by their underlying camera hardware. The first aim of my thesis research is to create a fast method of ocular wavefront sensing such that we can measure the wavefront aberrations at numerous points across a wide visual field. In order to address aim one, we will develop a sparse Zernike reconstruction technique (SPARZER) that will enable Shack-Hartmann wavefront sensors to use as little as 1/10th of the data that would normally be required for an accurate wavefront reading. If less data needs to be acquired, then we can increase the speed at which wavefronts can be recorded.

For my second aim, we will create a sophisticated optical model that reproduces the measured aberrations of the human eye. If we know how the average eye's optics distort light, then we can engineer ophthalmic imaging systems that preemptively cancel inherent ocular aberrations. This invention will help the retinal imaging community to design systems that are capable of acquiring high resolution images across a wide visual field. The proposed model eye is also of interest to the field of vision science as it aids in the study of how anatomy affects visual performance in the peripheral retina.

Using the optical model from aim two, we will design and reduce to practice a clinical OCT system that is capable of imaging a large (800) field-of-view with enhanced visualization of the peripheral retina. A key aspect of this third and final aim is to make the imaging system compatible with standard clinical practices. To this end, we will incorporate sensorless adaptive optics in order to correct the inter- and intra- patient variability in ophthalmic aberrations. Sensorless adaptive optics will improve both the brightness (signal) and clarity (resolution) of features in the peripheral retina without affecting the size of the imaging system.

The proposed work should not only be a noteworthy contribution to the ophthalmic and engineering communities, but it should strengthen our existing collaborations with the Duke Eye Center by advancing their capability to diagnose pathologies of the peripheral retinal.

Development of Plasmonic Nanoplatforms for Diagnostics, Therapy, and Sensing

Recent advances in nanotechnology have led to the application of nanoparticles in a wide variety of fields. In the field of nanomedicine, there is great emphasis on combining diagnostic and therapeutic modalities into a single nanoparticle construct (theranostics). In particular, anisotropic nanoparticles have shown great potential for surface-enhanced Raman scattering (SERS) detection due to their unique optical properties. Gold nanostars are a type of anisotropic nanoparticle with one of the highest SERS enhancement factors in a non-aggregated state. By utilizing the distinct characteristics of gold nanostars, new plasmonic materials for diagnostics, therapy, and sensing can be synthesized. The work described herein is divided into two main themes. The first half presents a novel, theranostic nanoplatform that can be used for both SERS detection and photodynamic therapy (PDT). The second half involves the rational design of silver-coated gold nanostars for increasing SERS signal intensity and improving reproducibility and quantification in SERS measurements.

The theranostic nanoplatforms consist of Raman-labeled gold nanostars coated with a silica shell. Photosensitizer molecules for PDT can be loaded into the silica matrix, while retaining the SERS signal of the gold nanostar core. SERS detection and PDT are performed at different wavelengths, so there is no interference between the diagnostic and therapeutic modalities. Singlet oxygen generation (a measure of PDT effectiveness) was demonstrated from the drug-loaded nanocomposites. In vitro testing with breast cancer cells showed that the nanoplatform could be successfully used for PDT. When further conjugating the nanoplatform with a cell-penetrating peptide (CPP), efficacy of both SERS detection and PDT is enhanced.

The rational design of plasmonic nanoparticles for SERS sensing involved the synthesis of silver-coated gold nanostars. Investigation of the silver coating process revealed that preservation of the gold nanostar tips was necessary to achieve the increased SERS intensity. At the optimal amount of silver coating, the SERS intensity is increased by over an order of magnitude. It was determined that a majority of the increased SERS signal can be attributed to reducing the inner filter effect, as the silver coating process moves the extinction of the particles far away from the laser excitation line. To improve reproducibility and quantitative SERS detection, an internal standard was incorporated into the particles. By embedding a small-molecule dye between the gold and silver surfaces, SERS signal was obtained both from the internal dye and external analyte on the particle surface. By normalizing the external analyte signal to the internal reference signal, reproducibility and quantitative analysis are improved in a variety of experimental conditions.

Retrieval of grassland biophysical parameters using multitemporal optical and radar satellite data

The amount and quality of available biomass is a key factor for the sustainable livestock industry and agricultural management related decision making. Globally 31.5% of land cover is grassland while 80% of Ireland’s agricultural land is grassland. In Ireland, grasslands are intensively managed and provide the cheapest feed source for animals. This dissertation presents a detailed state of the art review of satellite remote sensing of grasslands, and the potential application of optical (Moderate–resolution Imaging Spectroradiometer (MODIS)) and radar (TerraSAR-X) time series imagery to estimate the grassland biomass at two study sites (Moorepark and Grange) in the Republic of Ireland using both statistical and state of the art machine learning algorithms. High quality weather data available from the on-site weather station was also used to calculate the Growing Degree Days (GDD) for Grange to determine the impact of ancillary data on biomass estimation. In situ and satellite data covering 12 years for the Moorepark and 6 years for the Grange study sites were used to predict grassland biomass using multiple linear regression, Neuro Fuzzy Inference Systems (ANFIS) models. The results demonstrate that a dense (8-day composite) MODIS image time series, along with high quality in situ data, can be used to retrieve grassland biomass with high performance (R2 = 0:86; p < 0:05, RMSE = 11.07 for Moorepark). The model for Grange was modified to evaluate the synergistic use of vegetation indices derived from remote sensing time series and accumulated GDD information. As GDD is strongly linked to the plant development, or phonological stage, an improvement in biomass estimation would be expected. It was observed that using the ANFIS model the biomass estimation accuracy increased from R2 = 0:76 (p < 0:05) to R2 = 0:81 (p < 0:05) and the root mean square error was reduced by 2.72%. The work on the application of optical remote sensing was further developed using a TerraSAR-X Staring Spotlight mode time series over the Moorepark study site to explore the extent to which very high resolution Synthetic Aperture Radar (SAR) data of interferometrically coherent paddocks can be exploited to retrieve grassland biophysical parameters. After filtering out the non-coherent plots it is demonstrated that interferometric coherence can be used to retrieve grassland biophysical parameters (i. e., height, biomass), and that it is possible to detect changes due to the grass growth, and grazing and mowing events, when the temporal baseline is short (11 days). However, it not possible to automatically uniquely identify the cause of these changes based only on the SAR backscatter and coherence, due to the ambiguity caused by tall grass laid down due to the wind. Overall, the work presented in this dissertation has demonstrated the potential of dense remote sensing and weather data time series to predict grassland biomass using machine-learning algorithms, where high quality ground data were used for training. At present a major limitation for national scale biomass retrieval is the lack of spatial and temporal ground samples, which can be partially resolved by minor modifications in the existing PastureBaseIreland database by adding the location and extent ofeach grassland paddock in the database. As far as remote sensing data requirements are concerned, MODIS is useful for large scale evaluation but due to its coarse resolution it is not possible to detect the variations within the fields and between the fields at the farm scale. However, this issue will be resolved in terms of spatial resolution by the Sentinel-2 mission, and when both satellites (Sentinel-2A and Sentinel-2B) are operational the revisit time will reduce to 5 days, which together with Landsat-8, should enable sufficient cloud-free data for operational biomass estimation at a national scale. The Synthetic Aperture Radar Interferometry (InSAR) approach is feasible if there are enough coherent interferometric pairs available, however this is difficult to achieve due to the temporal decorrelation of the signal. For repeat-pass InSAR over a vegetated area even an 11 days temporal baseline is too large. In order to achieve better coherence a very high resolution is required at the cost of spatial coverage, which limits its scope for use in an operational context at a national scale. Future InSAR missions with pair acquisition in Tandem mode will minimize the temporal decorrelation over vegetation areas for more focused studies. The proposed approach complements the current paradigm of Big Data in Earth Observation, and illustrates the feasibility of integrating data from multiple sources. In future, this framework can be used to build an operational decision support system for retrieval of grassland biophysical parameters based on data from long term planned optical missions (e. g., Landsat, Sentinel) that will ensure the continuity of data acquisition. Similarly, Spanish X-band PAZ and TerraSAR-X2 missions will ensure the continuity of TerraSAR-X and COSMO-SkyMed.

Evaluating the spatial transferability and temporal repeatability of remote-sensing-based lake water quality retrieval algorithms at the European scale: a meta-analysis approach

Many studies have shown the considerable potential for the application of remote-sensing-based methods for deriving estimates of lake water quality. However, the reliable application of these methods across time and space is complicated by the diversity of lake types, sensor configuration, and the multitude of different algorithms proposed. This study tested one operational and 46 empirical algorithms sourced from the peer-reviewed literature that have individually shown potential for estimating lake water quality properties in the form of chlorophyll-a (algal biomass) and Secchi disc depth (SDD) (water transparency) in independent studies. Nearly half (19) of the algorithms were unsuitable for use with the remote-sensing data available for this study. The remaining 28 were assessed using the Terra/Aqua satellite archive to identify the best performing algorithms in terms of accuracy and transferability within the period 2001–2004 in four test lakes, namely Vänern, Vättern, Geneva, and Balaton. These lakes represent the broad continuum of large European lake types, varying in terms of eco-region (latitude/longitude and altitude), morphology, mixing regime, and trophic status. All algorithms were tested for each lake separately and combined to assess the degree of their applicability in ecologically different sites. None of the algorithms assessed in this study exhibited promise when all four lakes were combined into a single data set and most algorithms performed poorly even for specific lake types. A chlorophyll-a retrieval algorithm originally developed for eutrophic lakes showed the most promising results (R2 = 0.59) in oligotrophic lakes. Two SDD retrieval algorithms, one originally developed for turbid lakes and the other for lakes with various characteristics, exhibited promising results in relatively less turbid lakes (R2 = 0.62 and 0.76, respectively). The results presented here highlight the complexity associated with remotely sensed lake water quality estimates and the high degree of uncertainty due to various limitations, including the lake water optical properties and the choice of methods.

Synthesis, Modification, and Application of Siloxane Materials for Environmental Sensing

As human populations and resource consumption increase, it is increasingly important to monitor the quality of our environment. While laboratory instruments offer useful information, portable, easy to use sensors would allow environmental analysis to occur on-site, at lower cost, and with minimal operator training. We explore the synthesis, modification, and applications of modified polysiloxane in environmental sensing. Multiple methods of producing modified siloxanes were investigated. Oligomers were formed by using functionalized monomers, producing siloxane materials containing silicon hydride, methyl, and phenyl side chains. Silicon hydride-functionalized oligomers were further modified by hydrosilylation to incorporate methyl ester and naphthyl side chains. Modifications to the siloxane materials were also carried out using post-curing treatments. Methyl ester-functionalized siloxane was incorporated into the surface of a cured poly(dimethylsiloxane) film by siloxane equilibration. The materials containing methyl esters were hydrolyzed to reveal carboxylic acids, which could later be used for covalent protein immobilization. Finally, the siloxane surfaces were modified to incorporate antibodies by covalent, affinity, and adsorption-based attachment. These modifications were characterized by a variety of methods, including contact angle, attenuated total reflectance Fourier transform infrared spectroscopy, dye labels, and 1H nuclear magnetic resonance spectroscopy. The modified siloxane materials were employed in a variety of sensing schemes. Volatile organic compounds were detected using methyl, phenyl, and naphthyl-functionalized materials on a Fabry-Perot interferometer and a refractometer. The Fabry-Perot interferometer was found to detect the analytes upon siloxane extraction by deformation of the Bragg reflectors. The refractometer was used to determine that naphthyl-functionalized siloxanes had elevated refractive indices, rendering these materials more sensitive to some analytes. Antibody-modified siloxanes were used to detect biological analytes through a solid phase microextraction-mediated enzyme linked immunosorbent assay (SPME ELISA). The SPME ELISA was found to have higher analyte sensitivity compared to a conventional ELISA system. The detection scheme was used to detect Escherichia coli at 8500 CFU/mL. These results demonstrate the variety of methods that can be used to modify siloxanes and the wide range of applications of modified siloxanes has been demonstrated through chemical and biological sensing schemes.

Fiber-optic vibration sensing of cantilevers immersed in fluids

An all fiber-optical method to monitor densities and viscosities of liquids utilizing a steel cantilever (4 x 0.3 x 0.08 cm3) is presented. The actuation is performed by photothermally heating the cantilever at its base with an intensity-modulated 808 nm diode laser. The cantilever vibrations are picked up by an in-fiber Fabry Perot cavity sensor attached along the length of the cantilever. The fluid properties can be related to the resonance characteristics of the cantilever, e.g. a shift in the resonance frequency corresponds to a change in fluid density, and the width of the resonance peak gives information on the dynamic viscosity after calibration of the system. Aqueous glycerol, sucrose and ethanol samples in the range of 0.79–1.32 gcm−3 (density) and 0.89–702 mPas (viscosity) were used to investigate the limits of the sensor. A good agreement with literature values could be found with an average deviation of around 10 % for the dynamic viscosities, and 5–16 % for the mass densities. A variety of clear and opaque commercial spirits and an unknown viscous sample, e.g. home-made maple syrup, were analyzed and compared to literature values. The unique detection mechanism allows for the characterization of opaque samples and is superior to conventional microcantilever sensors. The method is expected to be beneficial in various industrial sectors such as quality control of food samples.

Refractometry Studies of the Optical Properties of Polymer Films and the Development of Polymer Coated Refractive Index Sensors

Sensors for real-time monitoring of environmental contaminants are essential for protecting ecosystems and human health. Refractive index sensing is a non-selective technique that can be used to measure almost any analyte. Miniaturized refractive index sensors, such as silicon-on-insulator (SOI) microring resonators are one possible platform, but require coatings selective to the analytes of interest. A homemade prism refractometer is reported and used to characterize the interactions between polymer films and liquid or vapour-phase analytes. A camera was used to capture both Fresnel reflection and total internal reflection within the prism. For thin-films (d = 10 μm - 100 μm), interference fringes were also observed. Fourier analysis of the interferogram allowed for simultaneous extraction of the average refractive index and film thickness with accuracies of ∆n = 1-7 ×10-4 and ∆d < 3-5%. The refractive indices of 29 common organic solvents as well as aqueous solutions of sodium chloride, sucrose, ethylene glycol, glycerol, and dimethylsulfoxide were measured at λ = 1550 nm. These measurements will be useful for future calibrations of near-infrared refractive index sensors. A mathematical model is presented, where the concentration of analyte adsorbed in a film can be calculated from the refractive index and thickness changes during uptake. This model can be used with Fickian diffusion models to measure the diffusion coefficients through the bulk film and at the film-substrate interface. The diffusion of water and other organic solvents into SU-8 epoxy was explored using refractometry and the diffusion coefficient of water into SU-8 is presented. Exposure of soft baked SU-8 films to acetone, acetonitrile and methanol resulted in rapid delamination. The diffusion of volatile organic compound (VOC) vapours into polydimethylsiloxane and polydimethyl-co-polydiphenylsiloxane polymers was also studied using refractometry. Diffusion and partition coefficients are reported for several analytes. As a model system, polydimethyl-co-diphenylsiloxane films were coated onto SOI microring resonators. After the development of data acquisition software, coated devices were exposed to VOCs and the refractive index response was assessed. More studies with other polymers are required to test the viability of this platform for environmental sensing applications.

Studies on Bio-Geochemistry, Bio-Optical Properties and Satellite Validation of Coastal Waters of South Eastern Arabian Sea

In situ methods used for water quality assessment have both physical and time constraints. Just a limited number of sampling points can be performed due to this, making it difficult to capture the range and variability of coastal processes and constituents. In addition, the mixing between fresh and oceanic water creates complex physical, chemical and biological environment that are difficult to understand, causing the existing measurement methodologies to have significant logistical, technical, and economic challenges and constraints. Remote sensing of ocean colour makes it possible to acquire information on the distribution of chlorophyll and other constituents over large areas of the oceans in short periods. There are many potential applications of ocean colour data. Satellite-derived products are a key data source to study the distribution pattern of organisms and nutrients (Guillaud et al. 2008) and fishery research (Pillai and Nair 2010; Solanki et al. 2001. Also, the study of spatial and temporal variability of phytoplankton blooms, red tide identification or harmful algal blooms monitoring (Sarangi et al. 2001; Sarangi et al. 2004; Sarangi et al. 2005; Bhagirathan et al., 2014), river plume or upwelling assessments (Doxaran et al. 2002; Sravanthi et al. 2013), global productivity analyses (Platt et al. 1988; Sathyendranath et al. 1995; IOCCG2006) and oil spill detection (Maianti et al. 2014). For remote sensing to be accurate in the complex coastal waters, it has to be validated with the in situ measured values. In this thesis an attempt to study, measure and validate the complex waters with the help of satellite data has been done. Monitoring of coastal ecosystem health of Arabian Sea in a synoptic way requires an intense, extensive and continuous monitoring of the water quality indicators. Phytoplankton determined from chl-a concentration, is considered as an indicator of the state of the coastal ecosystems. Currently, satellite sensors provide the most effective means for frequent, synoptic, water-quality observations over large areas and represent a potential tool to effectively assess chl-a concentration over coastal and oceanic waters; however, algorithms designed to estimate chl-a at global scales have been shown to be less accurate in Case 2 waters, due to the presence of water constituents other than phytoplankton which do not co-vary with the phytoplankton. The constituents of Arabian Sea coastal waters are region-specific because of the inherent variability of these optically-active substances affected by factors such as riverine input (e.g. suspended matter type and grain size, CDOM) and phytoplankton composition associated with seasonal changes.

Optics-Based Quantum Information and Sensing Platforms Utilizing the Nitrogen-Vacancy Center in Diamond

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

A compilation of global bio-optical in situ data for ocean-colour satellite applications

A compiled set of in situ data is important to evaluate the quality of ocean-colour satellite-data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GeP&CO), span between 1997 and 2012, and have a global distribution. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll a, spectral inherent optical properties and spectral diffuse attenuation coefficients. The data were from multi-project archives acquired via the open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were preserved throughout the work and made available in the final table. Using all the data in a validation exercise increases the number of matchups and enhances the representativeness of different marine regimes. By making available the metadata, it is also possible to analyse each set of data separately. The compiled data are available at doi: 10.1594/PANGAEA.854832 (Valente et al., 2015).

Aluminum nanoholes for optical biosensing

Sub-wavelength diameter holes in thin metal layers can exhibit remarkable optical features that make them highly suitable for (bio)sensing applications. Either as efficient light scattering centers for surface plasmon excitation or metal-clad optical waveguides, they are able to form strongly localized optical fields that can effectively interact with biomolecules and/or nanoparticles on the nanoscale. As the metal of choice, aluminum exhibits good optical and electrical properties, is easy to manufacture and process and, unlike gold and silver, its low cost makes it very promising for commercial applications. However, aluminum has been scarcely used for biosensing purposes due to corrosion and pitting issues. In this short review, we show our recent achievements on aluminum nanohole platforms for (bio)sensing. These include a method to circumvent aluminum degradation—which has been successfully applied to the demonstration of aluminum nanohole array (NHA) immunosensors based on both, glass and polycarbonate compact discs supports—the use of aluminum nanoholes operating as optical waveguides for synthesizing submicron-sized molecularly imprinted polymers by local photopolymerization, and a technique for fabricating transferable aluminum NHAs onto flexible pressure-sensitive adhesive tapes, which could facilitate the development of a wearable technology based on aluminum NHAs.

APPLICATION OF REMOTE SENSING IN AQUATIC ECOSYSTEMS

I utilized state the art remote sensing and GIS (Geographical Information System) techniques to study large scale biological, physical and ecological processes of coastal, nearshore, and offshore waters of Lake Michigan and Lake Superior. These processes ranged from chlorophyll a and primary production time series analysies in Lake Michigan to coastal stamp sand threats on Buffalo Reef in Lake Superior. I used SeaWiFS (Sea-viewing Wide Field-of-view Sensor) satellite imagery to trace various biological, chemical and optical water properties of Lake Michigan during the past decade and to investigate the collapse of early spring primary production. Using spatial analysis techniques, I was able to connect these changes to some important biological processes of the lake (quagga mussels filtration). In a separate study on Lake Superior, using LiDAR (Light Detection and Ranging) and aerial photos, we examined natural coastal erosion in Grand Traverse Bay, Michigan, and discussed a variety of geological features that influence general sediment accumulation patterns and interactions with migrating tailings from legacy mining. These sediments are moving southwesterly towards Buffalo Reef, creating a threat to the lake trout and lake whitefish breeding ground.

Silicon Photonic Device for Wavelength Sensing and Monitoring

Over the last decade advances and innovations from Silicon Photonics technology were observed in the telecommunications and computing industries. This technology which employs Silicon as an optical medium, relies on current CMOS micro-electronics fabrication processes to enable medium scale integration of many nano-photonic devices to produce photonic integrated circuitry. However, other fields of research such as optical sensor processing can benefit from silicon photonics technology, specially in sensors where the physical measurement is wavelength encoded. In this research work, we present a design and application of a thermally tuned silicon photonic device as an optical sensor interrogator. The main device is a micro-ring resonator filter of 10 $\mu m$ of diameter. A photonic design toolkit was developed based on open source software from the research community. With those tools it was possible to estimate the resonance and spectral characteristics of the filter. From the obtained design parameters, a 7.8 x 3.8 mm optical chip was fabricated using standard micro-photonics techniques. In order to tune a ring resonance, Nichrome micro-heaters were fabricated on top of the device. Some fabricated devices were systematically characterized and their tuning response were determined. From measurements, a ring resonator with a free-spectral-range of 18.4 nm and with a bandwidth of 0.14 nm was obtained. Using just 5 mA it was possible to tune the device resonance up to 3 nm. In order to apply our device as a sensor interrogator in this research, a model of wavelength estimation using time interval between peaks measurement technique was developed and simulations were carried out to assess its performance. To test the technique, an experiment using a Fiber Bragg grating optical sensor was set, and estimations of the wavelength shift of this sensor due to axial strains yield an error within 22 pm compared to measurements from spectrum analyzer. Results from this study implies that signals from FBG sensors can be processed with good accuracy using a micro-ring device with the advantage of ts compact size, scalability and versatility. Additionally, the system also has additional applications such as processing optical wavelength shifts from integrated photonic sensors and to be able to track resonances from laser sources.

Embedding silica and polymer fibre Bragg gratings (FBG) in plastic 3D-printed sensing patches

This paper reports the first demonstration of a silica fibre Bragg grating (SOFBG) embedded in an FDM 3-D printed housing to yield a dual grating temperature-compensated strain sensor. We also report the first ever integration of polymer fibre Bragg grating (POFBG) within a 3-D printed sensing patch for strain or temperature sensing. The cyclic strain performance and temperature characteristics of both devices are examined and discussed. The strain sensitivities of the sensing patches were 0.40 and 0.95 pm/μϵ for SOFBG embedded in ABS, 0.38 pm/μμ for POFBG in PLA, and 0.15 pm/μμ for POFBG in ABS. The strain response was linear above a threshold and repeatable. The temperature sensitivity of the SOFBG sensing patch was found to be up to 169 pm/°C, which was up to 17 times higher than for an unembedded silica grating. Unstable temperature response POFBG embedded in PLA was reported, with temperature sensitivity values varying between 30 and 40 pm/°C.