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This dissertation describes the development of a novel gamma-ray imaging system concept and presents results from Monte Carlo simulations of the new design. Current designs for large field-of-view gamma cameras suitable for homeland security applications implement either a coded aperture or a Compton scattering geometry to image a gamma-ray source. Both of these systems require large, expensive position-sensitive detectors in order to work effectively. By combining characteristics of both of these systems, a new design can be implemented that does not require such expensive detectors and that can be scaled down to a portable size. This new system has significant promise in homeland security, astronomy, botany and other fields, while future iterations may prove useful in medical imaging, other biological sciences and other areas, such as non-destructive testing. A proof-of-principle study of the new gamma-ray imaging system has been performed by Monte Carlo simulation. Various reconstruction methods have been explored and compared. General-Purpose Graphics-Processor-Unit (GPGPU) computation has also been incorporated. The resulting code is a primary design tool for exploring variables such as detector spacing, material selection and thickness and pixel geometry. The advancement of the system from a simple 1-dimensional simulation to a full 3-dimensional model is described. Methods of image reconstruction are discussed and results of simulations consisting of both a 4 x 4 and a 16 x 16 object space mesh have been presented. A discussion of the limitations and potential areas of further study is also presented.

It is well known that a translating mask can optically encode low-resolution measurements from which higher resolution images can be computationally reconstructed. We experimentally demonstrate that this principle can be used to achieve substantial increase in image resolution compared to the size of the focal plane array (FPA). Specifically, we describe a scalable architecture with a translating mask (also referred to as a coded aperture) that achieves eightfold resolution improvement (or 64: 1 increase in the number of pixels compared to the number of focal plane detector elements). The imaging architecture is described in terms of general design parameters (such as field of view and angular resolution, dimensions of the mask, and the detector and FPA sizes), and some of the underlying design trades are discussed. Experiments conducted with different mask patterns and reconstruction algorithms illustrate how these parameters affect the resolution of the reconstructed image. Initial experimental results also demonstrate that the architecture can directly support task-specific information sensing for detection and tracking, and that moving objects can be reconstructed separately from the stationary background using motion priors. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

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<p>This work focuses on the construction and application of coded apertures to compressive X-ray tomography. Coded apertures can be made in a number of ways, each method having an impact on system background and signal contrast. Methods of constructing coded apertures for structuring X-ray illumination and scatter are compared and analyzed. Apertures can create structured X-ray bundles that investigate specific sets of object voxels. The tailored bundles of rays form a code (or pattern) and are later estimated through computational inversion. Structured illumination can be used to subsample object voxels and make inversion feasible for low dose computed tomography (CT) systems, or it can be used to reduce background in limited angle CT systems. </p><p>On the detection side, coded apertures modulate X-ray scatter signals to determine the position and radiance of scatter points. By forming object dependent projections in measurement space, coded apertures multiplex modulated scatter signals onto a detector. The multiplexed signals can be inverted with knowledge of the code pattern and system geometry. This work shows two systems capable of determining object position and type in a 2D plane, by illuminating objects with an X-ray `fan beam,' using coded apertures and compressive measurements. Scatter tomography can help identify materials in security and medicine that may be ambiguous with transmission tomography alone.</p> / Dissertation

The physical properties of visible light and its interaction with matter create obstructions the human eye cannot explore. High energy radiation has been used as an alternative to visible light to penetrate these concealed regions and reveal their contents. However, traditional imaging techniques require a two-sided apparatus with a radiation source and a detector on opposite sides of the concealed object. One-sided imaging of concealed objects is made possible by a technique called backscatter imaging, utilizing high energy radiation. However, the signal produced by backscatter imaging is inherently weak, which makes in- terpretation di cult. One of the most promising techniques for recovering the weak signal is the coding and decoding provided by Coded Aperture Imaging (CAI). The purpose of this study was to create and test a coded aperture imaging system using backscattered x-rays. This would enable one-sided imaging of concealed objects and demonstrate whether a portable imaging system was feasible. The results obtained from conducting a computer simulation, visi- ble light experiments, and x-ray experiments proved that the process works, however, the x-ray ux levels required were too high for a portable system, based upon the current equipment available at UOIT. / UOIT

The BAT Slew Survey (BATSS) is the first wide-field survey of the hard X-ray sky (15–150 keV) with a slewing coded aperture imaging telescope. Its fine time resolution, high sensitivity and large sky coverage make it particularly well-suited for detections of transient sources with variability timescales in the \(\sim 1 sec–1 hour\) range, such as Gamma-Ray Bursts (GRBs), flaring stars and Blazars. As implemented, BATSS observations are found to be consistently more sensitive than their BAT pointing-mode counterparts, by an average of 20% over the 10 sec–3 ksec exposure range, due to intrinsic systematic differences between them. The survey’s motivation, development and implementation are presented, including a description of the software and hardware infrastructure that made this effort possible. The analysis of BATSS science data concentrates on the results of the 4.8-year BATSS GRB survey, beginning with the discovery of GRB 070326 during its preliminary testing phase. A total of nineteen (19) GRBs were detected exclusively in BATSS slews over this period, making it the largest contribution to the Swift GRB catalog from all ground-based analysis. The timing and spectral properties of prompt emission from BATSS GRBs reveal their consistency with Swift long GRBs (L-GRBs), though with instances of GRBs with unusually soft spectra or X-Ray Flashes (XRFs), GRBs near the faint end of the fluence distribution accessible to Swift-BAT, and a probable short GRB with extended emission, all uncommon traits within the general Swift GRB population. In addition, the BATSS overall detection rate of 0.49 GRBs/day of instrument time is a significant increase (45%) above the BAT pointing detection rate. This result was confirmed by a GRB detection simulation model, which further showed the increased sky coverage of slews to be the dominant effect in enhancing GRB detection probabilities. A review of lessons learned is included, with specific proposals to broaden both the number and range of astrophysical sources found in future enhancements. The BATSS survey results provide solid empirical evidence in support of an all-slewing hard X-ray survey mission, a prospect that may be realized with the launch of the proposed MIRAX-HXI mission in 2017. / Physics

<p>The emerging theory of compressed sensing has been nothing short of a revolution in signal processing, challenging some of the longest-held ideas in signal processing and leading to the development of exciting new ways to capture and reconstruct signals and images. Although the theoretical promises of compressed sensing are manifold, its implementation in many practical applications has lagged behind the associated theoretical development. Our goal is to elevate compressed sensing from an interesting theoretical discussion to a feasible alternative to conventional imaging, a significant challenge and an exciting topic for research in signal processing. When applied to imaging, compressed sensing can be thought of as a particular case of computational imaging, which unites the design of both the sensing and reconstruction of images under one design paradigm. Computational imaging tightly fuses modeling of scene content, imaging hardware design, and the subsequent reconstruction algorithms used to recover the images. </p><p>This thesis makes important contributions to each of these three areas through two primary research directions. The first direction primarily attacks the challenges associated with designing practical imaging systems that implement incoherent measurements. Our proposed snapshot imaging architecture using compressive coded aperture imaging devices can be practically implemented, and comes equipped with theoretical recovery guarantees. It is also straightforward to extend these ideas to a video setting where careful modeling of the scene can allow for joint spatio-temporal compressive sensing. The second direction develops a host of new computational tools for photon-limited inverse problems. These situations arise with increasing frequency in modern imaging applications as we seek to drive down image acquisition times, limit excitation powers, or deliver less radiation to a patient. By an accurate statistical characterization of the measurement process in optical systems, including the inherent Poisson noise associated with photon detection, our class of algorithms is able to deliver high-fidelity images with a fraction of the required scan time, as well as enable novel methods for tissue quantification from intraoperative microendoscopy data. In short, the contributions of this dissertation are diverse, further the state-of-the-art in computational imaging, elevate compressed sensing from an interesting theory to a practical imaging methodology, and allow for effective image recovery in light-starved applications.</p> / Dissertation

Le présent travail a pour but le développement d’une caméra gamma à masque codé permettant d’estimer la position tridimensionnelle (3D) des sources radioactives. Cela est d’un intérêt considérable dans le cadre d’un grand nombre d'applications, de la reconstruction de la forme 3D des objets radioactifs aux systèmes de réalité augmentée appliqués à la radioprotection. Les caméras gamma portables actuelles ne fournissent que la position angulaire relative des sources gamma à localiser, c'est-à-dire qu'aucune information métrique concernant les sources n’est disponible, comme par exemple leur distance par rapport à la caméra. Dans cette thèse, nous proposons principalement deux approches permettant d’estimer la position 3D des sources. La première approche consiste à étalonner la caméra gamma avec un capteur de profondeur à lumière structurée. La seconde approche permet d'estimer la distance source-détecteur par une méthode d’imagerie gamma stéréoscopique. Pour aligner géométriquement les images obtenues par la caméra gamma, le capteur de profondeur, et la caméra optique, une procédure d'étalonnage n’utilisant qu’une seule source ponctuelle radioactive a été conçue et mise en œuvre. Les résultats expérimentaux démontrent que les approches proposées permettent d'obtenir une précision inférieure au pixel, tant pour l’erreur de reprojection que pour la superposition des images gamma et optiques. Ces travaux présentent également une analyse quantitative de la précision et de la résolution relatives à l’estimation de la distance source-détecteur. De plus, les résultats obtenus ont validé le choix de la géométrie du modèle sténopé pour les caméras gamma à masque codé. / A coded aperture gamma camera for retrieving the three-dimensional (3-D) position of radioactive sources is presented. This is of considerable interest for a wide number of applications, ranging from the reconstruction of the 3-D shape of radioactive objects to augmented reality systems. Current portable γ-cameras only provide the relative angular position of the hotspots within their field of view. That is, they do not provide any metric information concerning the located sources. In this study, we propose two approaches to estimate the distance of the surrounding hotspots, and to autonomously determine if they are occluded by an object. The first consists in combining and accurately calibrating the gamma camera with a structured-light depth sensor. The second approach allows the estimation of the source-detector distance by means of stereo gamma imaging. To geometrically align the images obtained by the gamma, depth, and optical cameras used, a versatile calibration procedure has been designed and carried out. Such procedure uses a calibration phantom intentionally easy to build and inexpensive, allowing the procedure to be performed with only one radioactive point source. Experimental results showed that our calibration procedure yields to sub-pixel accuracy both in the re-projection error and the overlay of radiation and optical images. A quantitative analysis concerning the accuracy and resolution of the retrieved source-detector distance is also provided, along with an insight into the respective most influential factors. Moreover, the results obtained validated the choice of the geometry of the pinhole model for a coded aperture gamma camera.

Current x-ray technologies provide security personnel with non-invasive sub-surface imaging and contraband detection in various portal screening applications such as checked and carry-on baggage as well as cargo. Computed tomography (CT) scanners generate detailed 3D imagery in checked bags; however, these scanners often require significant power, cost, and space. These tomography machines are impractical for many applications where space and power are often limited such as checkpoint areas. Reducing the amount of data acquired would help reduce the physical demands of these systems. Unfortunately this leads to the formation of artifacts in various applications, thus presenting significant challenges in reconstruction and classification. As a result, the goal is to maintain a certain level of image quality but reduce the amount of data gathered. For the security domain this would allow for faster and cheaper screening in existing systems or allow for previously infeasible screening options due to other operational constraints. While our focus is predominantly on security applications, many of the techniques can be extended to other fields such as the medical domain where a reduction of dose can allow for safer and more frequent examinations. This dissertation aims to advance data reduction algorithms for security motivated x-ray imaging in three main areas: (i) development of a sensing aware dimensionality reduction framework, (ii) creation of linear motion tomographic method of object scanning and associated reconstruction algorithms for carry-on baggage screening, and (iii) the application of coded aperture techniques to improve and extend imaging performance of nuclear resonance fluorescence in cargo screening. The sensing aware dimensionality reduction framework extends existing dimensionality reduction methods to include knowledge of an underlying sensing mechanism of a latent variable. This method provides an improved classification rate over classical methods on both a synthetic case and a popular face classification dataset. The linear tomographic method is based on non-rotational scanning of baggage moved by a conveyor belt, and can thus be simpler, smaller, and more reliable than existing rotational tomography systems at the expense of more challenging image formation problems that require special model-based methods. The reconstructions for this approach are comparable to existing tomographic systems. Finally our coded aperture extension of existing nuclear resonance fluorescence cargo scanning provides improved observation signal-to-noise ratios. We analyze, discuss, and demonstrate the strengths and challenges of using coded aperture techniques in this application and provide guidance on regimes where these methods can yield gains over conventional methods.