Carbon nanotube (CNT)-based multibeam X-ray tubes provide an array of individually controllable X-ray focal spots. The CNT tube allows for flexible placement and distribution of X-ray focal spots in a system. Using a CNT tube, a computed tomography (CT) system with a noncircular geometry and a nonrotating gantry can be created. The noncircular CT geometry can be optimized around a specific imaging problem, utilizing the flexibility of CNT multibeam X-ray tubes to achieve the optimal focal spot distribution for the design constraints of the problem. Iterative reconstruction algorithms provide flexible CT reconstruction to accommodate the noncircular geometry. Compressed sensing-based iterative reconstruction algorithms apply a sparsity constraint to the reconstructed images that can partially account for missing angular coverage due to the noncircular geometry. In this paper, we present a laboratory prototype CT system that uses CNT multibeam X-ray tubes; a rectangular, nonrotating imaging geometry; and an accelerated compressed sensing-based iterative reconstruction algorithm. We apply a total variation minimization as our sparsity constraint. We present the advanced CNT multibeam tubes and show the stability and flexibility of these new tubes. We also present the unique imaging geometry and discuss the design constraints that influenced the specific system design. The reconstruction method is presented along with an overview of the acceleration of the algorithm to near real-time reconstruction. We demonstrate that the prototype reconstructed images have image quality comparable with a conventional CT system. The prototype is optimized for airport checkpoint baggage screening, but the concepts developed may apply to other application-specific CT imaging systems.INDEX TERMS Carbon nanotube x-ray sources, iterative reconstruction, compressed sensing reconstruction, total variation minimization, fixed-gantry computed tomography.

Recent developments in X-ray detectors have created the potential to perform energy-sensitive X-ray computed tomography (CT); that is, to reconstruct a series of CT images associated with different X-ray energies from a single scan. In this paper we propose a penalized weighted least squares (PWLS) algorithm for reconstruction of polychromatic energy-differentiated X-ray CT data and a unique experimental setup to take energy-differentiated X-ray CT data. The experimental setup is designed to acquire a complete X-ray spectrum for every projection ray. We use these data to estimate the linear attenuation coefficient as a function of energy for every pixel in the reconstructed attenuation map. We use prior knowledge of the properties of attenuation spectra to smooth the reconstructions, significantly reducing the noise and improving the contrast-to-noise ratio (CNR) in the reconstructed images without significantly biasing the data. We conclude that this algorithm is an effective method for reconstructing energy-sensitive CT data and provides justification for further research in energy sensitive CT systems.

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