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This pilot study examines the correlation of X-ray diffraction (XRD) measurements with the histopathological analysis of breast tissue. Eight breast cancer samples were investigated. Each sample contained a mixture of normal and cancerous tissues. In total, 522 separate XRD measurements were made at different locations across the samples (8 in total). The resulting XRD spectra were subjected to principal component analysis (PCA) in order to determine if there were any distinguishing features that could be used to identify different tissue components. 99.0% of the variation between the spectra were described by the first two principal components (PC). Comparing the location of points in PC space with the classification determined by histopathology indicated correlation between the shape/magnitude of the XRD spectra and the tissue type. These results are encouraging and suggest that XRD could be used for the intraoperative or postoperative classification of bulk tissue samples.

A system using energy-dispersive X-ray diffraction (EDXRD) has been developed and tested using multivariate calibration for the quantitative analysis of tablet-form mixtures of common pharmaceutical ingredients. A principal advantage of EDXRD over the more traditional and common angular dispersive X-ray diffraction technique (ADXRD) is the potential of EDXRD to analyse tablets within their packaging, due to the higher energy X-rays used.\ud \ud In the experiment, a series of caffeine, paracetamol and microcrystalline cellulose mixtures were prepared and pressed into tablets. EDXRD profiles were recorded on each sample and a principal component analysis (PCA) was carried out in both unpackaged and packaged scenarios. In both cases the first two principal components explained >98% of the between-sample variance. The PCA projected the sample profiles into two dimensional principal component space in close accordance to their ternary mixture design, demonstrating the discriminating potential of the EDXRD system.\ud \ud A partial least squares regression (PLSR) model was built with the samples and was validated using leave-one-out cross-validation. Low prediction errors of between 2% and 4% for both unpackaged and packaged tablets were obtained for all three chemical compounds. The prediction capability through packaging demonstrates a truly non-destructive method for quantifying tablet composition and demonstrates good potential for EDXRD to be applied in the field of counterfeit medicine screening and pharmaceutical quality control.

This paper introduces miniPixD: a new, compact system that utilises transmission X-ray imaging and X-ray diffraction (XRD) to locate and identify materials of interest within an otherwise opaque volume. The system and the embodied techniques have utility in security screening, medical diagnostics, non-destructive testing (NDT) and quality assurance (QA). This paper outlines the design of the system including discussion on the choice of components and presents some data from relevant samples which are compared to other energy dispersive and angular dispersive XRD techniques.

Identifying explosives in baggage at airports relies on being able to characterize the materials that make up an X-ray image. If a suspicion is generated during the imaging process (step 1), the image data could be enhanced by adapting the scanning parameters (step 2). This paper addresses the first part of this problem and uses textural signatures to recognize and characterize materials and hence enabling system control. Directional Gabor-type filtering was applied to a series of different X-ray images. Images were processed in such a way as to simulate a line scanning geometry. Based on our experiments with images of industrial standards and our own samples it was found that different materials could be characterized in terms of the frequency range and orientation of the filters. It was also found that the signal strength generated by the filters could be used as an indicator of visibility and optimum imaging conditions predicted.

In x-ray based baggage scanning, the ability to identify small devices (e.g. detonator components) and explosives in baggage or shipped parcels relies on being able to\ud characterize the materials and details that make up an x-ray image. Recently, an improvement over existing baggage\ud scanning techniques has been proposed in the form of a\ud system employing x-ray phase contrast imaging, as this was shown to detect smaller/fainter features and to be more sensitive to materials textures (small-scale inhomogeneites, etc).\ud This paper deals with additional image processing\ud performed on the phase contrast images produced by the\ud above system, to further improve its potential. It uses\ud textural analysis to enhance imaged micro-structures and\ud devices, and it has been found to be able to provide a\ud contrast increase of up to 300% on a series of images of a phantom mimicking the presence of an explosive device plus detonator components.

I-ImaS is a European project aiming to produce new, intelligent x-ray imaging systems using novel APS sensors to create optimal diagnostic images. Initial systems concentrate on mammography and encephalography. Later development will yield systems for other types of radiography such as industrial QA and homeland security. The I-ImaS system intelligence, due to APS technology and FPGAs, allows real-time analysis of data during image acquisition, giving the capability to build a truly adaptive imaging system with the potential to create images with maximum diagnostic information within given dose constraints. A companion paper deals with the DAQ system and preliminary characterization. This paper considers the laboratory x-ray characterization of the detector elements of the I-ImaS system. The characterization of the sensors when tiled to form a strip detector will be discussed, along with the appropriate correction techniques formulated to take into account the misalignments between individual sensors within the array. Preliminary results show that the detectors have sufficient performance to be used successfully in the initial mammographic and encephalographic I-ImaS systems under construction and this paper will further discuss the testing of these systems and the iterative processes used for intelligence upgrade in order to obtain the optimal algorithms and settings.

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Digital detectors based on complementary metal-oxide-semiconductors (CMOS) active pixel sensor (APS) technology have been introduced recently in many scientific applications. This work is focused on the X-ray performance evaluation of a novel CMOS APS detector in low energy medical imaging applications using monochromatic synchrotron radiation (i.e., 17-35 keV), which also allows studying how the performance varies with energy. The CMOS sensor was coupled to a Thallium-activated structured cesium iodide (CsI:Tl) scintillator and the detector's X-ray performance evaluation was carried out in terms of sensitivity, presampling modulation transfer function (pMTF), normalized noise power spectrum (NNPS) and the resulting detective quantum efficiency (DQE). A Monte Carlo simulation was used to validate the experimentally measured low frequency DQE. Finally, the effect of iodine's secondary generated K-fluorescence X-rays on pMTF and DQE results was evaluated. Good agreement (within 5%) was observed between the Monte Carlo and experimentally measured low frequency DQE results. A CMOS APS detector was characterized for the first time over a wide range of low energies covering the mammographic spectra. The detector's performance is limited mainly by the detectability of the scintillator. Finally, we show that the current data could be used to calculate the detector's pMTF, NNPS and DQE for any mammographic spectral shape within the investigated energies.

The aim of this project is the application of Dual Energy technique in breast phantoms using Complimentary-Metal-Oxide-Semiconductor (CMOS)Active-Pixel-Sensor (APS). This includes both, lab experimentation on developed breast phantoms, as well as simulations validating the results. Initially, phantoms were carefully prepared simulating the properties of real breast tissue and were imaged using X-ray unit. The next step in this project involved image processing and data representation. Using the dual energy technique, different concentrations of contrast agent (Iodine) were measured to relate clinical to medium kinetic measurements. With respect to this projects‟ clinical application, the implementation of this technique can be used to evaluate the iodine projected thickness (mg/cm2) using Contrast Enhanced Digital Mammography (CEDM) based on Dual Energy technique for the breast cancer detection. Ο στόχος του εργασίας αυτής είναι η εφαρμογή της τεχνικής Διπλής Ενέργειας σε ομοιώματα μαστού χρησιμοποιώντας Complimentary-Metal-Oxide-Semiconductor (CMOS) Active-Pixel-Sensor (APS). Αυτό περιλαμβάνει, τον πειραματισμό σε αναπτυγμένα ομοιώματα μαστού, καθώς και προσομοιώσεις για την επικύρωση των αποτελεσμάτων. Αρχικά, κατασκευάστηκαν προσεκτικά τα ομοιώματα μαστού όπου προσομοιώνουν τις πραγματικές ιδιότητες των ιστών του μαστού και στη συνέχεια απεικονίστηκαν με χρήση μονάδων ακτίνων-Χ . Το επόμενο βήμα σε αυτό την εργασία ήταν η επεξεργασία εικόνας και παρουσίαση δεδομένων. Χρησιμοποιώντας την τεχνική της διπλής ενέργειας, διαφορετικές συγκεντρώσεις σκιαγραφικού (ιώδιο) μετρήθηκαν ώστε να σχετίζουν κλινικά την μέτρηση της αγγειογένεσης εν συναρτήση του χρόνου. Η εφαρμογή αυτής της τεχνικής μπορεί να χρησιμοποιηθεί για να αξιολογήσει το προβλεπόμενο πάχος του ιωδίου (mg/cm^2) χρησιμοποιώντας Ενίσχυση Αντίθεσης στην Ψηφιακή Μαστογραφία βασίσμένη στην τεχνική της διπλής ενέργειας για την ανίχνευση του καρκίνου του μαστού.

I-ImaS is a European project aiming to produce new, intelligent x-ray imaging systems using novel APS sensors to create optimal diagnostic images. Initial systems concentrate on mammography and encephalography. Later development will yield systems for other types of radiography such as industrial QA and homeland security. The I-ImaS system intelligence, due to APS technology and FPGAs, allows real-time analysis of data during image acquisition, giving the capability to build a truly adaptive imaging system with the potential to create images with maximum diagnostic information within given dose constraints. A companion paper deals with the DAQ system and preliminary characterization. This paper considers the laboratory x-ray characterization of the detector elements of the I-ImaS system. The characterization of the sensors when tiled to form a strip detector will be discussed, along with the appropriate correction techniques formulated to take into account the misalignments between individual sensors within the array. Preliminary results show that the detectors have sufficient performance to be used successfully in the initial mammographic and encephalographic I-ImaS systems under construction and this paper will further discuss the testing of these systems and the iterative processes used for intelligence upgrade in order to obtain the optimal algorithms and settings