In this thesis, we demonstrate an array of photodetection theory and techniques bridging the traditional discrete and continuous variable experimental domains. In quantum optics, the creation and measurement of states of light are intertwined and we present experimental architectures considering both aspects. We describe the measurement of mean photon numbers at optical sideband frequencies using homodyne detection. We use our technique to provide a direct comparison to photon-counting measurements and observe that our technique exhibits superior speed, dynamic range and mode selectivity compared to photon counters. Our analysis also rejects a semiclassical description of the vacuum state, with our observations supporting the quantum mechanical model. We create a new means of describing the detection ???signatures??? of multi-port networks of non-photon-number discriminating detectors. Our model includes the practical effects of loss and dark counts. We use this model to analyse the performance of the loopand balanced- time-division-multiplexed detector architectures in a projective measurement role. Our analysis leads us to describe a prescriptive recipe for the optimisation of each architecture. In light of contemporary technology, we conclude the balanced TDM detector is the better architecture. Our analysis is then extended to the tomographic reconstruction of an unknown optical state using multi-port photon-counting networks. Our new approach is successfully applied to the reconstruction of the photon statistics of weak coherent states and demonstrates reduced error and sensitivity to experimental parameter variations than established techniques. We report the development of a source of quadrature squeezed vacuum at 1550 nm, and characterise the squeezing observed at the first 3 free spectral ranges of the downconversion cavity. This is then used as a source of frequency-entangled photons for a projective photon subtraction operation described by our earlier theory. We propose a new hybrid time/frequency domain approach to homodyne detection and illustrate its application in characterising the prepared state. Our output state has a statistically significant single photon contribution and permits future experimentation in frequency basis quantum information.

The Large Ultraviolet/Optical/Infrared Surveyor (LUVOIR) is one of four large mission concepts currently undergoing community study for consideration by the 2020 Astronomy and Astrophysics Decadal Survey. LUVOIR is being designed to pursue an ambitious program of exoplanetary discovery and characterization, cosmic origins astrophysics, and planetary science. The LUVOIR study team is investigating two large telescope apertures (9- and 15-meter primary mirror diameters) and a host of science instruments to carry out the primary mission goals. Many of the exoplanet, cosmic origins, and planetary science goals of LUVOIR require high-throughput, imaging spectroscopy at ultraviolet (100 - 400 nm) wavelengths. The LUVOIR Ultraviolet Multi-Object Spectrograph, LUMOS, is being designed to support all of the UV science requirements of LUVOIR, from exoplanet host star characterization to tomography of circumgalactic halos to water plumes on outer solar system satellites. LUMOS offers point source and multi-object spectroscopy across the UV bandpass, with multiple resolution modes to support different science goals. The instrument will provide low (R = 8,000 - 18,000) and medium (R = 30,000 - 65,000) resolution modes across the far-ultraviolet (FUV: 100 - 200 nm) and near-ultraviolet (NUV: 200 - 400 nm) windows, and a very low resolution mode (R = 500) for spectroscopic investigations of extremely faint objects in the FUV. Imaging spectroscopy will be accomplished over a 3 x 1.6 arcminute field-of-view by employing holographically-ruled diffraction gratings to control optical aberrations, microshutter arrays (MSA) built on the heritage of the Near Infrared Spectrograph (NIRSpec) on the James Webb Space Telescope (JWST), advanced optical coatings for high-throughput in the FUV, and next generation large-format photon-counting detectors. The spectroscopic capabilities of LUMOS are augmented by an FUV imaging channel (100 - 200nm, 13 milliarcsecond angular resolution, 2 x 2 arcminute field-of-view) that will employ a complement of narrow-and medium-band filters. The instrument definition, design, and development are being carried out by an instrument study team led by the University of Colorado, Goddard Space Flight Center, and the LUVOIR Science and Technology Definition Team. LUMOS has recently completed a preliminary design in Goddard's Instrument Design Laboratory and is being incorporated into the working LUVOIR mission concept. In this proceeding, we describe the instrument requirements for LUMOS, the instrument design, and technology development recommendations to support the hardware required for LUMOS. We present an overview of LUMOS' observing modes and estimated performance curves for effective area, spectral resolution, and imaging performance. Example "LUMOS 100-hour Highlights" observing programs are presented to demonstrate the potential power of LUVOIR's ultraviolet spectroscopic capabilities.

LIDAR is the optical equivalent of RADAR. A LIDAR facility has been established at Rhodes University using a flashlamp-pumped dye laser as the transmitter and a photomultiplier tube at the focus of a searchlight mirror as the receiver. The setting up of the receiver and transmitter as well as the design and construction of the photon counting electronics is described. The LIDAR has been used to measure aerosol scattering ratios and temperature profiles in the stratosphere and these results are presented with the algorithms and software used to reduce the data. Finally some recommendations are made for future work

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Nous avons travaillé sur le développement d’un instrument de mesure de la luminescence induite par un faisceau d’ions lourds (nucléons  12) et d’énergie de l’ordre du MeV/nucléons. Basé sur une méthode de comptage de photons uniques obtenus par coïncidences, le dispositif permet d’obtenir sur 16 voies à la fois un spectre en énergie dans le domaine proche UV-visible-proche IR (185-920 nm) et la réponse temporelle sur la gamme ns-µs, avec un échantillonnage de 100 ps. Des mesures en température peuvent être réalisées depuis la température ambiante jusqu’à 30K.Ce travail met particulièrement l’accent sur les méthodes d’extraction des données : Une fois montrée la nécessité de déconvoluer les signaux, on s’intéresse dans un premier temps à évaluer différents profils instrumentaux modélisés et reconstruit à partir de mesures. A cet effet, un travail de caractérisation temporelle de chaque constituant du dispositif est mené. Puis ces profils instrumentaux sont utilisés dans deux méthodes de déconvolution par moindres carrés d’abord puis par maximum d’entropie ensuite.Deux matériaux types sont testés : Le Titanate de Strontium pour l’étude de la dynamique de l’excitation électronique, et un scintillateur plastique commercial, le BC400, pour l’étude du vieillissement et de la baisse des performances en fonction de la fluence. Dans les deux cas on a pu mettre en évidence la présence d’une composante ultra rapide de constante de temps subnanoseconde. / We developed an instrument for measuring the luminescence induced by a heavy ion beam (nucleons  12) and energy in the range of MeV / nucleon. Based on a single photon counting method obtained by coincidences, the device can provide in the same run a 16-channel energy spectrum in the UV-visible- IR region (185-920 nm) and a time-resolved response in the range of ns up to µs for each channel. Temperature measurements can be performed from room temperature down to 30K.This work places particular emphasis on data extraction methods: Once the need to deconvolve the signals demonstrated the evaluation of different instrument profiles (simulated and reconstructed from measurements) leads to a systematic temporal characterization of each component of the device. Then, these instrumental profiles are used in two deconvolution methods: least squares first followed by maximum entropy method.Two typical materials are tested: the Strontium Titanate for the study of the dynamics of the electronic excitation, and a commercial scintillator, the BC400, for the study of the aging and the decrease of performances with fluence. In both cases, we have been able to highlight the presence of an ultrafast component of subnanosecond time constant.

High resolution transient/3D imaging technology is of high interest in both scientific research and commercial application. Nowadays, all of the transient imaging methods suffer from low resolution or time consuming mechanical scanning. We proposed a new method based on TCSPC and Compressive Sensing to achieve a high resolution transient imaging with a several seconds capturing process. Picosecond laser sends a serious of equal interval pulse while synchronized SPAD camera's detecting gate window has a precise phase delay at each cycle. After capturing enough points, we are able to make up a whole signal. By inserting a DMD device into the system, we are able to modulate all the frames of data using binary random patterns to reconstruct a super resolution transient/3D image later. Because the low fill factor of SPAD sensor will make a compressive sensing scenario ill-conditioned, We designed and fabricated a diffractive microlens array. We proposed a new CS reconstruction algorithm which is able to denoise at the same time for the measurements suffering from Poisson noise. Instead of a single SPAD senor, we chose a SPAD array because it can drastically reduce the requirement for the number of measurements and its reconstruction time. Further more, it not easy to reconstruct a high resolution image with only one single sensor while for an array, it just needs to reconstruct small patches and a few measurements. In this thesis, we evaluated the reconstruction methods using both clean measurements and the version corrupted by Poisson noise. The results show how the integration over the layers influence the image quality and our algorithm works well while the measurements suffer from non-trival Poisson noise. It's a breakthrough in the areas of both transient imaging and compressive sensing.

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X-ray Computed Tomography (CT) imaging has been playing a central role in clinical practice since it was invented in 1972. However, the traditional x-ray CT technique fails to distinguish different materials with similar density, especially for biological tissues. The lack of a quantitative imaging representation has constrained the application of CT technique from a broadening application such as personal or precision medicine. Therefore, my major thesis statement is to develop novel material-specific CT imaging techniques for molecular imaging in biological bodies. To achieve the goal, comprehensive studies were conducted to investigate three different techniques: x-ray fluorescence molecular imaging, material identification (specification) from photon counting CT, and photon counting CT data distortion correction approach based on deep learning. X-ray fluorescence molecular imaging (XFMI) has shown great promise as a low-cost molecular imaging modality for clinical and pre-clinical applications with high sensitivity. In this study, the effects of excitation beam spectrum on the molecular sensitivity of XFMI were experimentally investigated, by quantitatively deriving minimum detectable concentration (MDC) under a fixed surface entrance dose of 200 mR at three different excitation beam spectra. The result shows that the MDC can be readily increased by a factor of 5.26 via excitation spectrum optimization. Furthermore, a numerical model was developed and validated by the experimental data (≥0.976). The numerical model can be used to optimize XFMI system configurations to further improve the molecular sensitivity. Findings from this investigation could find applications for in vivo pre-clinical small-animal XFMI in the future. PCCT is an emerging technique that has the ability to distinguish photon energy and generate much richer image data that contains x-ray spectral information compared to conventional CT. In this study, a physics model was developed based on x-ray matter interaction physics to calculate the effective atomic number () and effective electron density () from PCCT image data for material identification. As the validation of the physics model, the and were calculated under various energy conditions for many materials. The relative standard deviations are mostly less than 1% (161 out of 168) shows that the developed model obtains good accuracy and robustness to energy conditions. To study the feasibility of applying the model with PCCT image data for material identification, both PCCT system numerical simulation and physical experiment were conducted. The result shows different materials can be clearly identified in the − map (with relative error ≤8.8%). The model has the value to serve as a material identification scheme for PCCT system for practical use in the future. As PCCT appears to be a significant breakthrough in CT imaging field, there exists severe data distortion problem in PCCT, which greatly limits the application of PCCT in practice. Lately, deep learning (DL) neural network has demonstrated tremendous success in medical imaging field. In this study, a deep learning neural network based PCCT data distortion correction method was proposed. When applying the algorithm to process the test dataset data, the accuracy of the PCCT data can be greatly improved (RMSE improved 73.7%). Compared with traditional data correction approaches such as maximum likelihood, the deep learning approach demonstrate superiority in terms of RMSE, SSIM, PSNR, and most importantly, runtime (4053.21 sec vs. 1.98 sec). The proposed method has the potential to facilitate the PCCT studies and applications in practice. / Doctor of Philosophy / X-ray Computed Tomography (CT) has played a central role in clinical imaging since it was invented in 1972. It has distinguishing characteristics of being able to generate three dimensional images with comprehensive inner structural information in fast speed (less than one second). However, traditional CT imaging lacks of material-specific capability due to the mechanism of image formation, which makes it cannot be used for molecular imaging. Molecular imaging plays a central role in present and future biomedical research and clinical diagnosis and treatment. For example, imaging of biological processes and molecular markers can provide unprecedented rich information, which has huge potentials for individualized therapies, novel drug design, earlier diagnosis, and personalized medicine. Therefore there exists a pressing need to enable the traditional CT imaging technique with material-specific capability for molecular imaging purpose. This dissertation conducted comprehensive study to separately investigate three different techniques: x-ray fluorescence molecular imaging, material identification (specification) from photon counting CT, and photon counting CT data distortion correction approach based on deep learning. X-ray fluorescence molecular imaging utilizes fluorescence signal to achieve molecular imaging in CT; Material identification can be achieved based on the rich image data from PCCT; The deep learning based correction method is an efficient approach for PCCT data distortion correction, and furthermore can boost its performance on material identification. With those techniques, the material-specific capability of CT can be greatly enhanced and the molecular imaging can be approached in biological bodies.

Computed tomography (CT) is a widely used medical imaging modality. By rotating an x-ray tube and an x-ray detector around the patient, a CT scanner is able to measure the x-ray transmission from all directions and form an image of the patient’s interior. CT scanners in clinical use today all use energy-integrating detectors, which measure the total incident energy for each measurement interval. A photon-counting detector, on the other hand, counts the number of incoming photons and can in addition measure the energy of each photon by comparing it to a number of energy thresholds. Using photon- counting detectors in computed tomography could lead to improved signal-to-noise ratio, higher spatial resolution and improved spectral imaging which allows better visualization of contrast agents and more reliable quantitative measurements. In this Thesis, the feasibility of using a photon-counting silicon-strip detector for CT is investigated. In the first part of the Thesis, the necessary performance requirements on such a detector is investigated in two different areas: the detector element homogeneity and the capability of handling high photon fluence rates. A metric of inhomogeneity is proposed and used in a simulation study to evaluate different inhomogeneity compensation methods. Also, the photon fluence rate incident on the detector in a scanner in clinical use today is investigated for different patient sizes through dose rate measurements together with simulations of transmission through patient im- ages. In the second part, a prototype detector module is used to demonstrate new applications enabled by the energy resolution of the detector. The ability to generate material-specific images of contrast agents with iodine and gadolinium is demonstrated. Furthermore, it is shown theoretically and ex- perimentally that interfaces in the image can be visualized by imaging the so-called nonlinear partial volume effect. The results suggest that the studied silicon-strip detector is a promising candidate for photon-counting CT.