The spectral X-ray imaging data acquisition (SpeXIDAQ) framework

Photon counting X-ray imagers have found their way into the mainstream scientific community in recent years, and have become important components in many scientific setups. These camera systems are in active development, with output data rates increasing significantly with every new generation of devices. A different class of PCD (Photon Counting Detector) devices has become generally available, where camera data output is no longer a matrix of photon counts but instead direct measurements of the deposited charge per pixel in every frame, which requires significant off-camera processing. This type of PCD, called a hyperspectral X-ray camera due to its fully spectroscopic output, yet again increases the demands put on the acquisition and processing backend. Not only are bandwidth requirements increased, but the need to do extensive data processing is also introduced with these hyperspectral PCD devices. To cope with these new developments the Spectral X-ray Imaging Data Acquisition framework (SpeXIDAQ) has been developed. All aspects of the imaging pipeline are handled by the SpeXIDAQ framework: from detector control and frame grabbing, to processing, storage and live visualisation during experiments

Component Separation for Spectral X-Ray Imaging Using the XPAD3 Hybrid Pixel Camera

The advent of hybrid pixel cameras in X-ray imaging opens the way to the acquisition of spectral measurements. These new devices for which photon counting replaces charge integration incorporate a dedicated readout electronic for each pixel including a capability of selecting energies via the setup of an energy threshold. This ability is of uppermost importance for the development of new polychromatic X-ray imaging approaches that will exploit spectral information on the detected X-rays. Spectral measurements in X-ray imaging pave the way to the separation of images in several components of physical and biological interest: the photoelectric and the Compton contributions can be separated while several contrast agents can be simultaneously localized. We investigate the capability to perform component separation by using the newly developed XPAD3 hybrid pixel camera incorporated in the micro-CT demonstrator PIXSCAN. Firstly, we propose an approach to configure the acquisition setup in order to optimize the component separation problem with respect to the robustness to the photon noise. The method is based on the Cramer-Rao Bound (CRB) that indicates the lowest reachable variance for the estimation of each component whatever the algorithm. Secondly, we investigate the separation problem with two components namely the photoelectric and the Compton ones. We show on noisy simulated data that such a separation with optimized setup i) enhances the contrast and the Contrast to Noise Ratio (CNR) between biological materials (adipose, soft tissues) and water; ii) cancels the artifacts of the beam-hardening effect that may strongly degrade the image quality. On going work involves two steps: first, dealing with Monte Carlo simulations and real data acquired with the PIXSCAN demonstrator; second, dealing with component separation with more than two components by adding several contrast agents, for which PIXSCAN has already proved its ability to separate them

Optimising the benefits of spectral x-ray imaging in material decomposition

The extra energy information provided by spectral x-ray imaging using novel photon counting x-ray detectors may allow for improved decomposition of materials compared to conventional and dual-energy imaging. The information content of spectral x-ray images, however, depends on how the photons are grouped together. This thesis deals with the theoretical aspect of optimising material discrimination in spectral x-ray imaging. A novel theoretical model was developed to map the confidence region of material thicknesses to determine the uncertainties in thickness quantification. Given the thickness uncertainties, photon counts per pixel can be optimised for material quantification in the most dose efficient manner. Minimisation of the uncertainties enables the optimisation of energy bins for material discrimination. Using Monte Carlo simulations based on the BEAMnrc package, material decomposition of up to 3 materials was performed on projection images, which led to the validation of the theoretical model. With the inclusion of scattered radiation, the theoretical optima of bin border energies were accurate to within 2 keV. For the simulated photon counts, excellent agreement was achieved between the theoretical and the BEAMnrc models regarding the signal-to-noise ratio in a decomposed image, particularly for the decomposition of two materials. Finally, this thesis examined the implementation of the Medipix detector. The equalisation of pixel sensitivity variations and the processing of photon counting projection images were studied. Measurements using the Medipix detector demonstrated promising results in the charge summing and the spectroscopic modes of acquisition, even though the spectroscopic performance of the detector was relatively limited due to electronic issues known to degrade the equalisation process. To conclude, the theoretical model is sufficient in providing guidelines for scanning parameters in spectral x-ray imaging and may be applied on spectral projection measurements using e.g. the redesigned MedipixRX detector with improved spectroscopic performance, when it becomes available

Window-Based Energy Selecting X-ray Imaging and Charge Sharing in Cadmium Zinc Telluride Linear Array Detectors for Contaminant Detection

The spectroscopic and imaging performance of energy-resolved photon counting detectors, based on new sub-millimetre boron oxide encapsulated vertical Bridgman cadmium zinc telluride linear arrays, are presented in this work. The activities are in the framework of the AVATAR X project, planning the development of X-ray scanners for contaminant detection in food industry. The detectors, characterized by high spatial (250 µm) and energy (<3 keV) resolution, allow spectral X-ray imaging with interesting image quality improvements. The effects of charge sharing and energy-resolved techniques on contrast-to-noise ratio (CNR) enhancements are investigated. The benefits of a new energy-resolved X-ray imaging approach, termed window-based energy selecting, in the detection of low- and high-density contaminants are also shown

Invertible Low-Dimensional Modelling of X-ray Absorption Spectra for Potential Applications in Spectral X-ray Imaging

X-ray interaction with matter is an energy-dependent process that is contingent on the atomic structure of the constituent material elements. The most advanced models to capture this relationship currently rely on Monte Carlo (MC) simulations. Whilst these very accurate models, in many problems in spectral X-ray imaging, such as data compression, noise removal, spectral estimation, and the quantitative measurement of material compositions, these models are of limited use, as these applications typically require the efficient inversion of the model, that is, they require the estimation of the best model parameters for a given spectral measurement. Current models that can be easily inverted however typically only work when modelling spectra in regions away from their K-edges, so they have limited utility when modelling a wider range of materials. In this paper, we thus propose a novel, non-linear model that combines a deep neural network autoencoder with an optimal linear model based on the Singular Value Decomposition (SVD). We compare our new method to other alternative linear and non-linear approaches, a sparse model and an alternative deep learning model. We demonstrate the advantages of our method over traditional models, especially when modelling X-ray absorption spectra that contain K-edges in the energy range of interest.Comment: 8 page

Experimental investigation of fast electron transport through Kα imaging and spectroscopy in relativistic laser-solid interactions

Abstract The study of the basic physical processes underlying the generation of fast electrons during the interaction of high-intensity short laser pulses with solid materials and the transport of these fast electrons through the target material are of great importance for the fast ignition concept for inertial confinement fusion and for the development of ultra-short X-ray sources. We report on the experimental investigation of fast electron transport phenomena by means of the spatial and spectral characterization of the X-ray emission from layered targets using bent crystal spectrometers and a new diagnostic technique based on a pinhole-camera equipped with a CCD detector working in single-photon regime for multi-spectral X-ray imaging The experiments were carried out at relativistic laser intensities, both in the longer (≃ps) pulse interaction regime relevant for fast ignition studie

Pulse pileup model for spectral resolved X-ray photon-counting detectors with dead time and retrigger capability

We developed an analytical model to evaluate the effect of signal pileup on the recorded energy spectrum in X-ray photon-counting detectors affected by dead time and equipped with retrigger capability. The retrigger function allows the system to work in a specific non-paralyzable counting mode by counting the time-over-threshold of piled-up signals in multiples of a predefined and selectable retrigger time. The model, designed for rectangle-like-shaped signals, allows for arbitrary input energy spectra and can significantly help understand and optimize the behavior of counting detectors with spectral capabilities and retrigger mechanisms in applications involving polychromatic beams, e.g., spectral X-ray imaging and computed tomography (CT), in a time-efficient way. Dedicated numerical simulations were used to validate the model under several conditions of incoming flux and threshold energy, with excellent results

MARS spectral molecular imaging of lamb tissue: data collection and image analysis

Spectral molecular imaging is a new imaging technique able to discriminate and quantify different components of tissue simultaneously at high spatial and high energy resolution. Our MARS scanner is an x-ray based small animal CT system designed to be used in the diagnostic energy range (20 to 140 keV). In this paper, we demonstrate the use of the MARS scanner, equipped with the Medipix3RX spectroscopic photon-processing detector, to discriminate fat, calcium, and water in tissue. We present data collected from a sample of lamb meat including bone as an illustrative example of human tissue imaging. The data is analyzed using our 3D Algebraic Reconstruction Algorithm (MARS-ART) and by material decomposition based on a constrained linear least squares algorithm. The results presented here clearly show the quantification of lipid-like, water-like and bone-like components of tissue. However, it is also clear to us that better algorithms could extract more information of clinical interest from our data. Because we are one of the first to present data from multi-energy photon-processing small animal CT systems, we make the raw, partial and fully processed data available with the intention that others can analyze it using their familiar routines. The raw, partially processed and fully processed data of lamb tissue along with the phantom calibration data can be found at [http://hdl.handle.net/10092/8531].Comment: 11 pages, 6 fig

The Effects of Extending the Spectral Information Acquired by a Photon-counting Detector for Spectral CT

Photon-counting x-ray detectors with pulse-height analysis provide spectral information that may improve material decomposition and contrast-to-noise ratio (CNR) in CT images. The number of energy measurements that can be acquired simultaneously on a detector pixel is equal to the number of comparator channels. Some spectral CT designs have a limited number of comparator channels, due to the complexity of readout electronics. The spectral information could be extended by changing the comparator threshold levels over time, sub pixels, or view angle. However, acquiring more energy measurements than comparator channels increases the noise and/or dose, due to differences in noise correlations across energy measurements and decreased dose utilisation. This study experimentally quantified the effects of acquiring more energy measurements than comparator channels using a bench-top spectral CT system. An analytical and simulation study modeling an ideal detector investigated whether there was a net benefit for material decomposition or optimal energy weighting when acquiring more energy measurements than comparator channels. Experimental results demonstrated that in a two-threshold acquisition, acquiring the high-energy measurement independently from the low-energy measurement increased noise standard deviation in material-decomposition basis images by factors of 1.5–1.7 due to changes in covariance between energy measurements. CNR in energy-weighted images decreased by factors of 0.92–0.71. Noise standard deviation increased by an additional factor of due to reduced dose utilisation. The results demonstrated no benefit for two-material decomposition noise or energy-weighted CNR when acquiring more energy measurements than comparator channels. Understanding the noise penalty of acquiring more energy measurements than comparator channels is important for designing spectral detectors and for designing experiments and interpreting data from prototype systems with a limited number of comparator channels