Modern optical astronomy: technology and impact of interferometry

The present `state of the art' and the path to future progress in high spatial resolution imaging interferometry is reviewed. The review begins with a treatment of the fundamentals of stellar optical interferometry, the origin, properties, optical effects of turbulence in the Earth's atmosphere, the passive methods that are applied on a single telescope to overcome atmospheric image degradation such as speckle interferometry, and various other techniques. These topics include differential speckle interferometry, speckle spectroscopy and polarimetry, phase diversity, wavefront shearing interferometry, phase-closure methods, dark speckle imaging, as well as the limitations imposed by the detectors on the performance of speckle imaging. A brief account is given of the technological innovation of adaptive-optics (AO) to compensate such atmospheric effects on the image in real time. A major advancement involves the transition from single-aperture to the dilute-aperture interferometry using multiple telescopes. Therefore, the review deals with recent developments involving ground-based, and space-based optical arrays. Emphasis is placed on the problems specific to delay-lines, beam recombination, polarization, dispersion, fringe-tracking, bootstrapping, coherencing and cophasing, and recovery of the visibility functions. The role of AO in enhancing visibilities is also discussed. The applications of interferometry, such as imaging, astrometry, and nulling are described. The mathematical intricacies of the various `post-detection' image-processing techniques are examined critically. The review concludes with a discussion of the astrophysical importance and the perspectives of interferometry.Comment: 65 pages LaTeX file including 23 figures. Reviews of Modern Physics, 2002, to appear in April issu

Vibrational Probe and Methods Development for Studying the Ultrafast Dynamics of Preferential Solvation of Biomolecules by 2D-IR.

Over the last decade two-dimensional infrared spectroscopy (2D-IR) has emerged as a powerful method for the investigation of biological samples and their dynamics. Through the implementation of state of the art signal processing methods we have demonstrated a significant, 20-fold, reduction in the acquisition time of traditional 2D-IR spectra. This new technique, utilizing compressed sensing, compliments our previously developed RASD method, allowing for the rapid acquisition of complete 2D-IR spectra as opposed to dynamical information at a single excitation-detection frequency pair. Additionally we have realized the first biocompatible, modular, metal-carbonyl probes for 2D-IR utilizing benzyl-chromium tribarbonyls. This has enabled ultrafast 2D-IR investigations of lipids and preferential solvation in solutions and at site-specific locations within enzyme scaffolds. In aqueous solutions we find that preferential solvation by a polar cosolvent causes a slowdown of the observed dynamics sensed by our probes. From modeling our system this slowdown is found to be consistent with arising from the slow, ca. 8 ps, exchange dynamics between the polar co-solute and water in the vicinity of our probe. This interpretation of preferential solvation in solution is further able to describe the observed dynamical differences found at the protein-solvent interface in a model system. By studying a series of protein mutants we find, spectroscopically and through simulations, that interactions between the side chains and the solution are sufficient to modulate the degree of preferential solvation and therefore dynamics, within specific sites of the protein. This information provides a foundation on how to modulate of the diffusion of substrates and products into and out-of the active sites of enzymes, through directed mutation of their protein sequence. The diffusional motion of the solvent and substrates is often the rate-limiting step in enzymatic catalysis. By controlling the local solvation dynamics of enzymes, sequence mutations offer a method to fine-tune the dynamics of enzymes. The ability to characterize the site-specific solvation dynamics of enzymes in response to primary structure mutations, positions 2D-IR and our chromium tricarbonyl probes as powerful tools for understanding protein and enzyme dynamics. This provides insight into controlling the catalytic rate of enzymes through directed mutation.PhDBiophysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111440/1/josefd_1.pd

Hyperspectral Tomographic FTIR Imaging Using Two Illumination Geometries for Polymer Phantoms

The purpose of this dissertation is to carry out non-destructive 3D imaging by applying Fourier Transform Infrared (FTIR) spectro-microtomographic techniques, and develop corresponding methods of data analysis. This is done by collecting 3D synchrotron-based and lab-based (Thermal) FTIR hyper spectral data at the Synchrotron Radiation Center (SRC) for the first time. Despite other 2D imaging techniques, this does not manipulate the sample, and suppresses the need to microtome 3D biological, material or biomedical samples into slices to study by spectroscopic imaging techniques. Spectro-micro-tomography is applicable for scientific, industrial, energy, biomedical samples such as stem cell characterization and materials such as polymers. Tomographic reconstruction methods are employed to the data to investigate the chemical and morphological localization, and obtain the average spectra of regions of interest as well as spectra for every voxel. It is assumed that the thermal light has cone geometry, and the data collected needs cone beam reconstruction, whereas the data collected using synchrotron light requires parallel beam reconstruction, since the beam waist created by the focus at IR wavelengths of the synchrotron 12 beams can be approximated well by a parallel beam. While bright synchrotron light provides us with higher SNR data, the capability of doing FTIR spectro-micro-tomographic techniques using thermal light, processing and analyzing it is of a high significance since thermal sources are more readily available. In this study the cone beam reconstruction is implemented and evaluated by applying them to the phantoms such as centered and off-center Polystyrene beads, and samples of mixed-polymers. The results of the cone beam reconstruction show that the cone beam reconstruction does not improve the quality of the reconstruction, and the parallel beam reconstruction is still better. The cone beam is not capable of modelling the optical system of our imaging environment, and the half cone beam angle size is small enough to be considered as parallel beam. Furthermore, the application of the cone beam is limited to the size of the sample. For further analysis of the 3D reconstructed volumes of the samples, specific signal processing tools are required. The deconvolution algorithm is applied to the 2D projections at all the wavelengths before the reconstruction to increase the image contrast and spectral fidelity, deblur the projections, and finally increase the contrast of the 3D images. Segmentation methods will be implemented for defining the regions of interest in the 3D structures; this will be used for average spectrum computation as a necessary tool of spectral analysis. The techniques developed here employ thresholding and kmeans clustering are capable of calculating the average spectra of the components found in the data as well as their corresponding renderings

Magnetic resonance spectroscopic imaging with 2D spectroscopy for the detection of brain metabolites

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis. Page 94 blank.Includes bibliographical references (p. 87-93).While magnetic resonance imaging (MRI) derives its signal from protons in water, additional biochemical compounds are detectable in vivo within the proton spectrum. The detection and mapping of these much weaker signals is known as magnetic resonance spectroscopy or spectroscopic imaging. Among the complicating factors for this modality applied to human clinical imaging are limited chemical-shift dispersion and J-coupling, which cause spectral overlap and complicated spectral shapes that limit detection and separation of brain metabolites using MR spectroscopic imaging (MRSI). Existing techniques for improved detection include so-called 2D spectroscopy, where additional encoding steps aid in the separation of compounds with overlapping chemical shift. This is achieved by collecting spectral data over a range of timing parameters and introducing an additional frequency axis. While these techniques have been shown to improve signal separation, they carry a penalty in scan time that is often prohibitive when combined with MRSI. Beyond scan time constraints, the lipid signal contamination from the subcutaneous tissue in the head pose problems in MRSI. Due to the large voxel size typical in MRSI experiments, ringing artifacts from lipid signals become more prominent and contaminate spectra in brain tissue. This is despite the spatial separation of subcutaneous and brain tissue. This thesis first explores the combination of a 2D MRS method, _Constant Time Point REsolved SpectroScopy (CT-PRESS) with fast spiral encoding in order to achieve feasible scan times for human in-vivo scanning. Human trials were done on a 3.OT scanner and with a 32-channel receive coil array. A lipid contamination minimization algorithm was incorporated for the reduction of lipid artifacts in brain metabolite spectra. This method was applied to the detection of cortical metabolites in the brain and results showed that peaks of metabolites, glutamate, glutamine and N-acetyl-aspartate were recovered after successful lipid suppression. The second task of this thesis was to investigate under-sampling in the indirect time dimension of CT-PRESS and its associated reconstruction with Multi-Task Bayesian Compressed Sensing, which incorporated fully-sampled simulated spectral data as prior information for regularization. It was observed that MT Bayesian CS gave good reconstructions despite simulated incomplete prior knowledge of spectral parameters.by Trina Kok.Ph.D

Mineral identification using data-mining in hyperspectral infrared imagery

Les applications de l’imagerie infrarouge dans le domaine de la géologie sont principalement des applications hyperspectrales. Elles permettent entre autre l’identification minérale, la cartographie, ainsi que l’estimation de la portée. Le plus souvent, ces acquisitions sont réalisées in-situ soit à l’aide de capteurs aéroportés, soit à l’aide de dispositifs portatifs. La découverte de minéraux indicateurs a permis d’améliorer grandement l’exploration minérale. Ceci est en partie dû à l’utilisation d’instruments portatifs. Dans ce contexte le développement de systèmes automatisés permettrait d’augmenter à la fois la qualité de l’exploration et la précision de la détection des indicateurs. C’est dans ce cadre que s’inscrit le travail mené dans ce doctorat. Le sujet consistait en l’utilisation de méthodes d’apprentissage automatique appliquées à l’analyse (au traitement) d’images hyperspectrales prises dans les longueurs d’onde infrarouge. L’objectif recherché étant l’identification de grains minéraux de petites tailles utilisés comme indicateurs minéral -ogiques. Une application potentielle de cette recherche serait le développement d’un outil logiciel d’assistance pour l’analyse des échantillons lors de l’exploration minérale. Les expériences ont été menées en laboratoire dans la gamme relative à l’infrarouge thermique (Long Wave InfraRed, LWIR) de 7.7m à 11.8 m. Ces essais ont permis de proposer une méthode pour calculer l’annulation du continuum. La méthode utilisée lors de ces essais utilise la factorisation matricielle non négative (NMF). En utlisant une factorisation du premier ordre on peut déduire le rayonnement de pénétration, lequel peut ensuite être comparé et analysé par rapport à d’autres méthodes plus communes. L’analyse des résultats spectraux en comparaison avec plusieurs bibliothèques existantes de données a permis de mettre en évidence la suppression du continuum. Les expérience ayant menés à ce résultat ont été conduites en utilisant une plaque Infragold ainsi qu’un objectif macro LWIR. L’identification automatique de grains de différents matériaux tels que la pyrope, l’olivine et le quartz a commencé. Lors d’une phase de comparaison entre des approches supervisées et non supervisées, cette dernière s’est montrée plus approprié en raison du comportement indépendant par rapport à l’étape d’entraînement. Afin de confirmer la qualité de ces résultats quatre expériences ont été menées. Lors d’une première expérience deux algorithmes ont été évalués pour application de regroupements en utilisant l’approche FCC (False Colour Composite). Cet essai a permis d’observer une vitesse de convergence, jusqu’a vingt fois plus rapide, ainsi qu’une efficacité significativement accrue concernant l’identification en comparaison des résultats de la littérature. Cependant des essais effectués sur des données LWIR ont montré un manque de prédiction de la surface du grain lorsque les grains étaient irréguliers avec présence d’agrégats minéraux. La seconde expérience a consisté, en une analyse quantitaive comparative entre deux bases de données de Ground Truth (GT), nommée rigid-GT et observed-GT (rigide-GT: étiquet manuel de la région, observée-GT:étiquetage manuel les pixels). La précision des résultats était 1.5 fois meilleur lorsque l’on a utlisé la base de données observed-GT que rigid-GT. Pour les deux dernières epxérience, des données venant d’un MEB (Microscope Électronique à Balayage) ainsi que d’un microscopie à fluorescence (XRF) ont été ajoutées. Ces données ont permis d’introduire des informations relatives tant aux agrégats minéraux qu’à la surface des grains. Les résultats ont été comparés par des techniques d’identification automatique des minéraux, utilisant ArcGIS. Cette dernière a montré une performance prometteuse quand à l’identification automatique et à aussi été utilisée pour la GT de validation. Dans l’ensemble, les quatre méthodes de cette thèse représentent des méthodologies bénéfiques pour l’identification des minéraux. Ces méthodes présentent l’avantage d’être non-destructives, relativement précises et d’avoir un faible coût en temps calcul ce qui pourrait les qualifier pour être utilisée dans des conditions de laboratoire ou sur le terrain.The geological applications of hyperspectral infrared imagery mainly consist in mineral identification, mapping, airborne or portable instruments, and core logging. Finding the mineral indicators offer considerable benefits in terms of mineralogy and mineral exploration which usually involves application of portable instrument and core logging. Moreover, faster and more mechanized systems development increases the precision of identifying mineral indicators and avoid any possible mis-classification. Therefore, the objective of this thesis was to create a tool to using hyperspectral infrared imagery and process the data through image analysis and machine learning methods to identify small size mineral grains used as mineral indicators. This system would be applied for different circumstances to provide an assistant for geological analysis and mineralogy exploration. The experiments were conducted in laboratory conditions in the long-wave infrared (7.7μm to 11.8μm - LWIR), with a LWIR-macro lens (to improve spatial resolution), an Infragold plate, and a heating source. The process began with a method to calculate the continuum removal. The approach is the application of Non-negative Matrix Factorization (NMF) to extract Rank-1 NMF and estimate the down-welling radiance and then compare it with other conventional methods. The results indicate successful suppression of the continuum from the spectra and enable the spectra to be compared with spectral libraries. Afterwards, to have an automated system, supervised and unsupervised approaches have been tested for identification of pyrope, olivine and quartz grains. The results indicated that the unsupervised approach was more suitable due to independent behavior against training stage. Once these results obtained, two algorithms were tested to create False Color Composites (FCC) applying a clustering approach. The results of this comparison indicate significant computational efficiency (more than 20 times faster) and promising performance for mineral identification. Finally, the reliability of the automated LWIR hyperspectral infrared mineral identification has been tested and the difficulty for identification of the irregular grain’s surface along with the mineral aggregates has been verified. The results were compared to two different Ground Truth(GT) (i.e. rigid-GT and observed-GT) for quantitative calculation. Observed-GT increased the accuracy up to 1.5 times than rigid-GT. The samples were also examined by Micro X-ray Fluorescence (XRF) and Scanning Electron Microscope (SEM) in order to retrieve information for the mineral aggregates and the grain’s surface (biotite, epidote, goethite, diopside, smithsonite, tourmaline, kyanite, scheelite, pyrope, olivine, and quartz). The results of XRF imagery compared with automatic mineral identification techniques, using ArcGIS, and represented a promising performance for automatic identification and have been used for GT validation. In overall, the four methods (i.e. 1.Continuum removal methods; 2. Classification or clustering methods for mineral identification; 3. Two algorithms for clustering of mineral spectra; 4. Reliability verification) in this thesis represent beneficial methodologies to identify minerals. These methods have the advantages to be a non-destructive, relatively accurate and have low computational complexity that might be used to identify and assess mineral grains in the laboratory conditions or in the field

Reconstruction algorithms for Magnetic Resonance Imaging

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 135-142).This dissertation presents image reconstruction algorithms for Magnetic Resonance Imaging (MRI) that aims to increase the imaging efficiency. Algorithms that reduce imaging time without sacrificing the image quality and mitigate image artifacts are proposed. The goal of increasing the MR efficiency is investigated across multiple imaging techniques: structural imaging with multiple contrasts preparations, Diffusion Spectrum Imaging (DSI), Chemical Shift Imaging (CSI), and Quantitative Susceptibility Mapping (QSM). The main theme connecting the proposed methods is the utilization of prior knowledge on the reconstructed signal. This prior often presents itself in the form of sparsity with respect to either a prespecified or learned signal transformation.by Berkin Bilgic.Ph.D

Reconstruction algorithms for multispectral diffraction imaging

Thesis (Ph.D.)--Boston UniversityIn conventional Computed Tomography (CT) systems, a single X-ray source spectrum is used to radiate an object and the total transmitted intensity is measured to construct the spatial linear attenuation coefficient (LAC) distribution. Such scalar information is adequate for visualization of interior physical structures, but additional dimensions would be useful to characterize the nature of the structures. By imaging using broadband radiation and collecting energy-sensitive measurement information, one can generate images of additional energy-dependent properties that can be used to characterize the nature of specific areas in the object of interest. In this thesis, we explore novel imaging modalities that use broadband sources and energy-sensitive detection to generate images of energy-dependent properties of a region, with the objective of providing high quality information for material component identification. We explore two classes of imaging problems: 1) excitation using broad spectrum sub-millimeter radiation in the Terahertz regime and measure- ment of the diffracted Terahertz (THz) field to construct the spatial distribution of complex refractive index at multiple frequencies; 2) excitation using broad spectrum X-ray sources and measurement of coherent scatter radiation to image the spatial distribution of coherent-scatter form factors. For these modalities, we extend approaches developed for multimodal imaging and propose new reconstruction algorithms that impose regularization structure such as common object boundaries across reconstructed regions at different frequencies. We also explore reconstruction techniques that incorporate prior knowledge in the form of spectral parametrization, sparse representations over redundant dictionaries and explore the advantage and disadvantages of these techniques in terms of image quality and potential for accurate material characterization. We use the proposed reconstruction techniques to explore alternative architectures with reduced scanning time and increased signal-to-noise ratio, including THz diffraction tomography, limited angle X-ray diffraction tomography and the use of coded aperture masks. Numerical experiments and Monte Carlo simulations were conducted to compare performances of the developed methods, and validate the studied architectures as viable options for imaging of energy-dependent properties

Development of a handheld fiber-optic probe-based raman imaging instrumentation: raman chemlighter

Raman systems based on handheld fiber-optic probes offer advantages in terms of smaller sizes and easier access to the measurement sites, which are favorable for biomedical and clinical applications in the complex environment. However, there are several common drawbacks of applying probes for many applications: (1) The fixed working distance requires the user to maintain a certain working distance to acquire higher Raman signals; (2) The single-point-measurement ability restricts realizing a mapping or scanning procedure; (3) Lack of real-time data processing and a straightforward co-registering method to link the Raman information with the respective measurement position. The thesis proposed and experimentally demonstrated various approaches to overcome these drawbacks. A handheld fiber-optic Raman probe with an autofocus unit was presented to overcome the problem arising from using fixed-focus lenses, by using a liquid lens as the objective lens, which allows dynamical adjustment of the focal length of the probe. An implementation of a computer vision-based positional tracking to co-register the regular Raman spectroscopic measurements with the spatial location enables fast recording of a Raman image from a large tissue sample by combining positional tracking of the laser spot through brightfield images. The visualization of the Raman image has been extended to augmented and mixed reality and combined with a 3D reconstruction method and projector-based visualization to offer an intuitive and easily understandable way of presenting the Raman image. All these advances are substantial and highly beneficial to further drive the clinical translation of Raman spectroscopy as potential image-guided instrumentation

Vibrational spectroscopy as a tool to understand plant silicification

Die Ablagerung von Siliziumdioxid ist ein verbreitetes Phänomen, das mit der Toleranz von Pflanzen gegenüber Belastungen korreliert. Die Pflanzen akkumulieren das amorphe Siliziumdioxid in mikroskopischen Partikeln, den Phytolithen, jedoch ist der exakte Mechanismus nicht vollständig aufgeklärt. Um ein besseres Verständnis über die Ablagerung von Siliziumdioxid zu erlangen, wurden verschiedene spektroskopische Techniken an Sorghumblättern und molekularen Modellen angewandt. Festkörper Kernspinresonanz und thermogravimetrische Analysen zeigen, dass die Siliziumdioxidstruktur von der Phytolithe-Extraktion abhängt. Basierend auf Raman- und IR-Daten einzelner Phytolithe lassen sich die Änderungen dieser Strukturen ermitteln. Das deutet auf unterschiedliche biologische Prozesse der Ablagerung des Siliciumdioxids hin. Die Pflanzengewebe in denen Siliciumdioxid abgelagert ist, wurden mit einem multimodalen Ansatz charakterisiert, welcher Fluoreszenz-, Hellfeld- und Rasterelektronenmikroskopie beinhaltet. Die chemische Zusammensetzung der Pflanzengewebe wurden mit Raman- und FTIR-Mikrospektroskopie kartiert. Ein neuartiger Ansatz zur Untersuchung von Pflanzengeweben wurde verwendet, basierend auf der optischen Nahfeldmikroskopie im mittleren IR-Bereich. Dieser ermöglicht eine kombinierte Analyse von mechanischen Materialeigenschaften sowie der chemischen Zusammensetzung und Struktur. Um die Rolle der organischen Matrix zu verstehen, wurden Modellverbindungen betrachtet, die die Ablagerung von Kieselsäure in den Pflanzen induzieren können. In-vitro-Reaktionen konnten eine gleichzeitige Präzipitation von Lignin und Siliciumdioxid sowie eine Polymerisation zusammen mit Peptiden simulieren. Die Ergebnisse lassen starke Wechselwirkungen zwischen diesen Verbindungen vermuten. Neben einem besseren Verständnis verschiedener Aspekte der Silifizierung von Pflanzen werden in dieser Arbeit neue Methoden zur Charakterisierung von Pflanzenproben vorgeschlagen.Silica deposition is a common phenomenon that correlates with plant tolerance to various stresses. Plants accumulate amorphous silica in microscopic particles termed phytoliths, through yet unclear mechanisms. With the aim to gain better understanding of the processes that govern silica deposition, different vibrational techniques were used on sorghum leaves and molecular models to obtain chemical and structural information addressing different length scales. Solid-state Nuclear Magnetic Resonance and thermogravimetric analysis showed that phytolith extraction methods affect silica structure. Nevertheless, Raman and IR analysis of individual phytoliths revealed differences in the structure and composition between phytolith types, suggesting the existence of different biological pathways for silica deposition. The environment of sorghum tissues where silica is deposited was assessed using a multimodal approach consisting of fluorescence, brightfield and scanning electron microscopies, while chemical composition was mapped using Raman and Fourier transformed Infrared microspectroscopy. Scattering-type near-field optical microscopy in the mid-infrared region was used to characterize the plant tissues, in both fixed and native plant samples. The nano-IR images and the mechanical phase image enabled a combined probing of mechanical material properties together with the chemical composition and structure of both the cell walls and the phytolith structures. In vitro reactions simulating lignin-silica co-precipitation and silica polymerization with peptides revealed strong interaction between these compounds and silica, and their possible involvement in silica deposition in the plant. This thesis provides a better understanding of the chemical process that control plant silicification, suggests new methodologies to characterize plant samples, and evaluates the current methods used in plant science