Excessive imaging doses from repeated scans and poor image quality mainly due to scatter contamination are the two bottlenecks of cone-beam CT (CBCT) imaging. This study investigates a method that combines measurement-based scatter correction and a compressed sensing (CS)-based iterative reconstruction algorithm to generate scatter-free images from low-dose data. Scatter distribution is estimated by interpolating/extrapolating measured scatter samples inside blocked areas. CS-based iterative reconstruction is finally carried out on the under-sampled data to obtain scatter-free and low-dose CBCT images. In the tabletop phantom studies, with only 25% dose of a conventional CBCT scan, our method reduces the overall CT number error from over 220 HU to less than 25 HU, and increases the image contrast by a factor of 2.1 in the selected ROIs. Dual-energy CT (DECT) is another important application of CBCT. DECT shows promise in differentiating materials that are indistinguishable in single-energy CT and facilitates accurate diagnosis. A general problem of DECT is that decomposition is sensitive to noise in the two sets of projection data, resulting in severely degraded qualities of decomposed images. The first study of DECT is focused on the linear decomposition method. In this study, a combined method of iterative reconstruction and decomposition is proposed. The noise on the two initial CT images from separate scans becomes well correlated, which avoids noise accumulation during the decomposition process. To fully explore the benefits of DECT on beam-hardening correction and to reduce the computation cost, the second study is focused on an iterative decomposition method with a non-linear decomposition model for noise suppression in DECT. Phantom results show that our methods achieve superior performance on DECT imaging, with respect to noise reduction and spatial resolution.

published_or_final_version / abstract / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

X-ray crystallography is a technique for determining the structure (positions of atoms in space) of molecules. It is a well developed technique, and is applied routinely to both small inorganic and large organic molecules. However, the determination of the structures of large biological molecules by x-ray crystallography can still be an experimentally and computationally expensive task. The data in an x-ray experiment are the amplitudes of the Fourier transform of the electron density in the crystalline specimen. The structure determination problem in x-ray crystallography is therefore identical to a phase retrieval problem in image reconstruction, for which iterative transform algorithms are a common solution method. This thesis is concerned with iterative projection algorithms, a generalized and more powerful version of iterative transform algorithms, and their application to macromolecular x-ray crystallography. A detailed study is made of iterative projection algorithms, including their properties, convergence, and implementations. Two applications to macromolecular crystallography are then investigated. The first concerns reconstruction of binary image and the application of iterative projection algorithms to determining molecular envelopes from x-ray solvent contrast variation data. An effective method for determining molecular envelopes is developed. The second concerns the use of symmetry constraints and the application of iterative projection algorithms to ab initio determination of macromolecular structures from crystal diffraction data. The algorithm is tested on an icosahedral virus and a protein tetramer. The results indicate that ab initio phasing is feasible for structures containing 4-fold or 5-fold non-crystallographic symmetry using these algorithms if an estimate of the molecular envelope is available.

In this dissertation, imaging characteristics of a nano-slit are investigated. Applications of a scanning and rotating nano-slit in measuring sub-micron aerial features are demonstrated. Coherent sub-micron spot distributions are reconstructed with a very high contrast. Finally, high NA partially coherent images with features as small as 210 nm half-pitch are reconstructed and the ultimate resolution of the system is determined.A nano-slit is characterized as a sensor for coherent line-and-space features. Experiments and simulation verify image detection with contrasts greater than 0.9. Effects of polarization on imaging performance are reported. A scanning and rotating nano-slit in conjunction with a filtered back-projection technique is used to reconstruct sub-micron coherent spot distributions. Simulation results show very good agreement with the experiment. Further, it is shown that the reconstruction is very resilient to some common random experimental errors.Imaging characteristics of a scanning nano-slit sensor are determined for high NA partially coherent images. Good imaging performance (contrast > 0.8) is demonstrated with line-and-space images up to a spatial frequency of 2.38 lp / micron. Sub-micron features in a high NA partially coherent image are measured with a scanning and rotating nano-slit. A modified microscope is used to create the measured features, including 210 nm half-pitch features that cannot be imaged using the microscope in a conventional imaging mode. Using the filtered back projection technique, two-dimensional sub-micron features are reconstructed by the nano-slit sensor. It is determined that the resolution limit of ~ 200 nm is determined by the reconstruction technique and not by the width of the nano-slit.

The work presented in this dissertation involves the development of parametric magnetic resonance imaging (MRI) techniques that can be used in a clinical set up. In the first chapter an introduction of basic magnetic resonance physics is given. The introduction covers the source to tissue magnetization, the origin of the detectable signal, the relaxation mechanisms, and the imaging principles. In the second chapter T₂ estimation - the main parametric MRI technique addressed in this work - is introduced and the problem associated with T₂ estimation from highly undersampled fast spin-echo (FSE) data is presented. In Chapter 3, a novel model-based algorithm with linearization by principal component analysis (REPCOM) is described. Based on simulations, physical phantom and in vivo data, the proposed algorithm is shown to produce accurate and stable T₂ estimates. In Chapter 4, the concept of indirect echoes associated with the acquisition of FSE data is introduced. Indirect echo correction using the extended phase graph approach is then studied for standard sampled data. A novel reconstruction algorithm (SERENADE) is presented for the reconstruction of decay curves with indirect echoes from highly undersampled data. The technique is evaluated using simulations, physical phantom and in vivo data; decay curves with indirect echoes are shown to be accurately recovered by this technique. Chapter 5 is dedicated to correcting the partial volume effect (PVE) in T₂ estimation. For the case of small lesions within a background tissue, PVE affects T₂ estimation which in turn affects lesion classification. A novel joint fitting algorithm is proposed and compared to conventional fitting algorithms using fully sampled spin-echo (SE) images. It is shown that the proposed algorithm is more accurate, robust, and insensitive to region of interest drawing than the conventional fitting algorithms. Because the acquisition of fully sampled SE images is long, the technique is combined with a thick refocusing slice approach in order to be able to use undersampled FSE data and reduce the acquisition time to a breath hold (~ 20 s). The final chapter summarizes the results presented in the dissertations and discusses areas for future work.

<p>This dissertation studies the coding strategies of computational imaging to overcome the limitation of conventional sensing techniques. The information capacity of conventional sensing is limited by the physical properties of optics, such as aperture size, detector pixels, quantum efficiency, and sampling rate. These parameters determine the spatial, depth, spectral, temporal, and polarization sensitivity of each imager. To increase sensitivity in any dimension can significantly compromise the others. </p><p>This research implements various coding strategies subject to optical multidimensional imaging and acoustic sensing in order to extend their sensing abilities. The proposed coding strategies combine hardware modification and signal processing to exploiting bandwidth and sensitivity from conventional sensors. We discuss the hardware architecture, compression strategies, sensing process modeling, and reconstruction algorithm of each sensing system. </p><p>Optical multidimensional imaging measures three or more dimensional information of the optical signal. Traditional multidimensional imagers acquire extra dimensional information at the cost of degrading temporal or spatial resolution. Compressive multidimensional imaging multiplexes the transverse spatial, spectral, temporal, and polarization information on a two-dimensional (2D) detector. The corresponding spectral, temporal and polarization coding strategies adapt optics, electronic devices, and designed modulation techniques for multiplex measurement. This computational imaging technique provides multispectral, temporal super-resolution, and polarization imaging abilities with minimal loss in spatial resolution and noise level while maintaining or gaining higher temporal resolution. The experimental results prove that the appropriate coding strategies may improve hundreds times more sensing capacity. </p><p>Human auditory system has the astonishing ability in localizing, tracking, and filtering the selected sound sources or information from a noisy environment. Using engineering efforts to accomplish the same task usually requires multiple detectors, advanced computational algorithms, or artificial intelligence systems. Compressive acoustic sensing incorporates acoustic metamaterials in compressive sensing theory to emulate the abilities of sound localization and selective attention. This research investigates and optimizes the sensing capacity and the spatial sensitivity of the acoustic sensor. The well-modeled acoustic sensor allows localizing multiple speakers in both stationary and dynamic auditory scene; and distinguishing mixed conversations from independent sources with high audio recognition rate.</p> / Dissertation

University of Macau / Faculty of Science and Technology. / Department of Computer and Information Science

Incoherent scatter radar (ISR) systems allow researchers to peer into the ionosphere via remote sensing of intrinsic plasma parameters. ISR sensors have been used since the 1950s and until the past decade were mainly equipped with a single mechanically steerable antenna. As such, the ability to develop a two or three dimensional picture of the plasma parameters in the ionosphere has been constrained by the relatively slow mechanical steering of the antennas. A newer class of systems using electronically steerable array (ESA) antennas have broken the chains of this constraint, allowing researchers to create 3-D reconstructions of plasma parameters. There have been many studies associated with reconstructing 3-D fields of plasma parameters, but there has not been a systematic analysis into the sampling issues that arise. Also, there has not been a systematic study as to how to reconstruct these plasma parameters in an optimum sense as opposed to just using different forms of interpolation. The research presented here forms a framework that scientists and engineers can use to plan experiments with ESA ISR capabilities and to better analyze the resulting data. This framework attacks the problem of space-time sampling by ESA ISR systems from the point of view of signal processing, simulation and inverse theoretic image reconstruction. We first describe a physics based model of incoherent scatter from the ionospheric plasma, along with processing methods needed to create the plasma parameter measurements. Our approach leads to development of the space-time ambiguity function, forming a theoretical foundation of the forward model for ISR. This forward model is novel in that it takes into account the shape of the antenna beam and scanning method along with integration time to develop the proper statistics for a desired measurement precision. Once the forward model is developed, we present the simulation method behind the Simulator for ISR (SimISR). SimISR uses input plasma parameters over space and time and creates complex voltage samples in a form similar to that produced by a real ISR system. SimISR allows researchers to evaluate different experiment configurations in order to efficiently and accurately sample specific phenomena. We present example simulations using input conditions derived from a multi-fluid ionosphere model and reconstructions using standard interpolation techniques. Lastly, methods are presented to invert the space-time ambiguity function using techniques from image reconstruction literature. These methods are tested using SimISR to quantify accurate plasma parameter reconstruction over a simulated ionospheric region.

The PRESTO sequence is a well-known 3-D fMRI imaging sequence. In this sequence the echo planar imaging technique is merged with the echo-shift technique. This combination results in a very fast image acquisition, which is required for fMRI examinations of neural activation in the human brain. The aim of this work was to use the basic Cartesian PRESTO sequence as a framework when developing a novel trajectory using a non-Cartesian grid. Our new pulse sequence, PRESTO CAN, rotates the k-space profiles around the ky-axis in a non-Cartesian manner. This results in a high sampling density close to the centre of the k-space, and at the same time it provides sparser data collection of the part of the k-space that contains less useful information. This "can- or cylinder-like" pattern is expected to result in a much faster k-space acquisition without loosing important spatial information. A new reconstruction algorithm was also developed. The purpose was to be able to construct an image volume from data obtained using the novel PRESTO CAN sequence. This reconstruction algorithm was based on the gridding technique, and a Kaiser-Bessel window was also used in order to re-sample the data onto a Cartesian grid. This was required to make 3-D Fourier transformation possible. In addition, simulations were also performed in order to verify the function of the reconstruction algorithm. Furthermore, in vitro tests showed that the development of the PRESTO CAN sequence and the corresponding reconstruction algorithm were highly successful. In the future, the results can relatively easily be extended and generalized for in vivo investigations. In addition, there are numerous exciting possibilities for extending the basic techniques described in this thesis.