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X-ray imaging is a nondestructive and preferred method in paleopathology to reconstruct the history of ancient diseases. Sophisticated imaging technologies such as computed tomography (CT) have become common for the investigation of skeletal disorders in human remains. Researchers have investigated the impact of ionizing radiation on living cells, but never on ancient cells in dry tissue. The effects of CT exposure on ancient cells have not been examined in the past and may be important for subsequent genetic analysis. To remedy this shortcoming, we developed different Monte Carlo models to simulate X-ray irradiation on ancient cells. Effects of mummification were considered by using two sizes of cells and three different phantom tissues, which enclosed the investigated cell cluster. This cluster was positioned at the isocenter of a CT scanner model, where the cell hit probabilities P(0,1, , n) were calculated according to the Poisson distribution. To study the impact of the dominant physics process, CT scans for X-ray spectra of 80 and 120 kVp were simulated. Comparison between normal and dry tissue phantoms revealed that the probability of unaffected cells increased by 21% following cell shrinkage for 80 kVp, while for 120 kVp, a further increase of unaffected cells of 23% was observed. Consequently, cell shrinkage caused by dehydration decreased the impact of X-ray radiation on mummified cells significantly. Moreover, backscattered electrons in cortical bone protected deeper-lying ancient cells from radiation damage at 80 kVp X-rays

The first step that is required to extract the correct information from a two-dimensional (2D) diffraction signature is to remove the background accurately. However, direct background subtraction inevitably overcorrects the signal as it does not take into account the attenuation by the sample. Other traditional background removal methods, such as the rolling ball technique, can separate sharp diffraction peaks of crystalline materials from their background. These methods are unsuitable for biological tissue, which is amorphous and does not have sharp diffraction peaks. This technical note proposes a novel method that combines peak fitting and experimental results to estimate the background for 2D XRD signals.

A variation of the RadICAL (Radiation Imaging Cylinder Activity Locator) system capable of operating in a dynamic environment, such as that created by active interrogation techniques, has been developed. RadICAL is a novel method for locating a radiological source using a rotating detector element. The detector geometry is that of a thin sheet and is rotated to present a constantly changing surface area to the source; it therefore generates a characteristic temporal response which can be used to determine the source direction. The time required to determine the direction of a source make it unsuitable for dynamic environments and so an alternative method is presented that uses a stack of identical scintillator slabs positioned at fixed horizontal angles around a central axis. By comparing count rates from each slab to a standard response curve, using a specially developed algorithm, the direction of a source can be determined without the need to rotate the detector. EJ-299-33 plastic scintillator was used to allow detection of separate neutron and gamma events in a mixed field through pulse shape discrimination. A four element detector was built and shown to achieve a positional accuracy of approximately 4.4 degrees when exposed to a 1.44MBq 137 Cs source at distances of up to 2m. The same detector was used to discriminate separate neutron and gamma events in a mixed field, which allows for the possibility of locating a neutron source within a gamma rich environment.

Energy dispersive X‐ray diffraction (EDXRD) and maximum likelihood principal component analysis multivariate curve resolution‐alternating least squares (MLPCA‐MCR‐ALS) with correlation constraint were used to quantify the composition of packaged pharmaceutical formulations. Recorded EDXRD profiles from unpackaged and packaged samples of ternary mixtures were modelled together in order to recover the concentrations as well as the pure profiles of the constituent compounds. MLPCA was used as a data pretreatment step to MCR‐ALS, accounting for the high noise and nonconstant variance observed in the EDXRD profiles and was shown to improve the resolution accuracy of MCR‐ALS for the data set. Local correlation constraints were applied in the MCR‐ALS procedure in order to model unpackaged and packaged samples simultaneously while accounting for the matrix effect of the packaging materials. The composition of the formulations was estimated with root‐mean‐square error of prediction for each component, including paracetamol, being approximately 2.5 %w/w for unpackaged and packaged samples. Paracetamol concentration was resolved simultaneously for the unpackaged and packaged samples to a greater degree of accuracy than achieved by partial least squares regression (PLSR) when modelling the contexts separately. By modelling the effects of the packaging and incorporating accurate reference information of unpackaged samples into the resolution of packaged samples, the potential of EDXRD and MLPCA‐MCR‐ALS for the identification and quantification of packaged solid‐dosage medicine in nondestructive screening and counterfeit medicine detection has been raised.

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. 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. 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 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.

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.

Recently CMOS Active Pixels Sensors (APSs) have become a valuable alternative to amorphous Silicon and Selenium Flat Panel Imagers (FPIs) in bio-medical imaging applications. CMOS APSs can now be scaled up to the standard 20 cm diameter wafer size by means of a reticle stitching block process. However despite wafer scale CMOS APS being monolithic, sources of non-uniformity of response and regional variations can persist representing a significant challenge for wafer scale sensor response. Non-uniformity of stitched sensors can arise from a number of factors related to the manufacturing process, including variation of amplification, variation between readout components, wafer defects and process variations across the wafer due to manufacturing processes. This paper reports on an investigation into the spatial non-uniformity and regional variations of a wafer scale stitched CMOS APS. For the first time a per-pixel analysis of the electro-optical performance of a wafer CMOS APS is presented, to address inhomogeneity issues arising from the stitching techniques used to manufacture wafer scale sensors. A complete model of the signal generation in the pixel array has been provided and proved capable of accounting for noise and gain variations across the pixel array. This novel analysis leads to readout noise and conversion gain being evaluated at pixel level, stitching block level and in regions of interest, resulting in a coefficient of variation ≤ 1.9%. The uniformity of the image quality performance has been further investigated in a typical X-ray application, i.e. mammography, showing a uniformity in terms of CNR among the highest when compared with mammography detectors commonly used in clinical practise. Finally, in order to compare the detection capability of this novel APS with the currently used technology (i.e. FPIs), theoretical evaluation of the Detection Quantum Efficiency (DQE) at zero-frequency has been performed, resulting in a higher DQE for this detector

Energy dispersive X‐ray diffraction (EDXRD) and maximum likelihood principal component analysis multivariate curve resolution‐alternating least squares (MLPCA‐MCR‐ALS) with correlation constraint were used to quantify the composition of packaged pharmaceutical formulations. Recorded EDXRD profiles from unpackaged and packaged samples of ternary mixtures were modelled together in order to recover the concentrations as well as the pure profiles of the constituent compounds. MLPCA was used as a data pretreatment step to MCR‐ALS, accounting for the high noise and nonconstant variance observed in the EDXRD profiles and was shown to improve the resolution accuracy of MCR‐ALS for the data set. Local correlation constraints were applied in the MCR‐ALS procedure in order to model unpackaged and packaged samples simultaneously while accounting for the matrix effect of the packaging materials. The composition of the formulations was estimated with root‐mean‐square error of prediction for each component, including paracetamol, being approximately 2.5 %w/w for unpackaged and packaged samples. Paracetamol concentration was resolved simultaneously for the unpackaged and packaged samples to a greater degree of accuracy than achieved by partial least squares regression (PLSR) when modelling the contexts separately. By modelling the effects of the packaging and incorporating accurate reference information of unpackaged samples into the resolution of packaged samples, the potential of EDXRD and MLPCA‐MCR‐ALS for the identification and quantification of packaged solid‐dosage medicine in nondestructive screening and counterfeit medicine detection has been raised.

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. 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. 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 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.

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.

X-ray imaging is a nondestructive and preferred method in paleopathology to reconstruct the history of ancient diseases. Sophisticated imaging technologies such as computed tomography (CT) have become common for the investigation of skeletal disorders in human remains. Researchers have investigated the impact of ionizing radiation on living cells, but never on ancient cells in dry tissue. The effects of CT exposure on ancient cells have not been examined in the past and may be important for subsequent genetic analysis. To remedy this shortcoming, we developed different Monte Carlo models to simulate X-ray irradiation on ancient cells. Effects of mummification were considered by using two sizes of cells and three different phantom tissues, which enclosed the investigated cell cluster. This cluster was positioned at the isocenter of a CT scanner model, where the cell hit probabilities P(0,1, , n) were calculated according to the Poisson distribution. To study the impact of the dominant physics process, CT scans for X-ray spectra of 80 and 120 kVp were simulated. Comparison between normal and dry tissue phantoms revealed that the probability of unaffected cells increased by 21% following cell shrinkage for 80 kVp, while for 120 kVp, a further increase of unaffected cells of 23% was observed. Consequently, cell shrinkage caused by dehydration decreased the impact of X-ray radiation on mummified cells significantly. Moreover, backscattered electrons in cortical bone protected deeper-lying ancient cells from radiation damage at 80 kVp X-rays