Spectral computed tomography is an emerging imaging method that involves using recently developed energy discriminating photon-counting detectors (PCDs). This technique enables measurements at isolated high-energy ranges, in which the dominating undergoing interaction between the x-ray and the sample is the incoherent scattering. The scattered radiation causes a loss of contrast in the results, and its correction has proven to be a complex problem, due to its dependence on energy, material composition, and geometry. Monte Carlo simulations can utilize a physical model to estimate the scattering contribution to the signal, at the cost of high computational time. We present a fast Monte Carlo simulation tool, based on McXtrace, to predict the energy resolved radiation being scattered and absorbed by objects of complex shapes. We validate the tool through measurements using a CdTe single PCD (Multix ME-100) and use it for scattering correction in a simulation of a spectral CT. We found the correction to account for up to 7% relative amplification in the reconstructed linear attenuation. It is a useful tool for x-ray CT to obtain a more accurate material discrimination, especially in the high-energy range, where the incoherent scattering interactions become prevailing (>50 keV).
In X-ray computed tomography (CT), scattered radiation plays an important role in the accurate reconstruction of the inspected object, leading to a loss of contrast between the different materials in the reconstruction volume and cupping artifacts in the images. We present a Monte Carlo simulation tool for spectral X-ray CT to predict the scattered radiation generated by complex samples. An experimental setup is presented to isolate the energy distribution of scattered radiation. Spectral CT is a novel technique implementing photon-counting detectors able to discriminate the energy of incoming photons, enabling spectral analysis of X-ray images. This technique is useful to extract efficiently more information on energy dependent quantities (e.g. mass attenuations coefficients) and study matter interactions (e.g. X-ray scattering, photoelectric absorption, etc...). Having a good knowledge of the spectral distribution of the scattered X-rays is fundamental to establish methods attempting to correct for it. The simulations are validated by real measurements using a CdTe spectral resolving detector (Multix ME-100). We observed the effect of the scattered radiation on the image reconstruction, becoming relevant in the energy range where the Compton events are dominant (i.e. above 50keV).
We will present examples of applying the X-ray tracing software package McXtrace to different kinds of X-ray
scattering experiments. In particular we will be focusing on time-resolved type experiments. Simulations of full
scale experiments are particularly useful for this kind, especially when they are performed at an FEL-facility.
Beamtime here is extremely scarce and the delay between experiment and publication is notoriously long. A
major cause for the delay is the general complexity of the experiments performed. A complexity which arises
from the pulsed state of the source.
As an example, consider a pump-and-probe type experiment. In order to get the wanted signal from the
sample the X-ray pulse from the FEL source needs to overlap in space and time with the pumping pulse inside the
sample. This is made more difficult by several effects: The sample response may be dependent of the polarisation
of the pumping and/or probing pulse. There may be significant time-jitter in the pulse arrival times. The
composition of the sample may vary depending on local sample geometry and be modified by the probing pulse.
Many of the samples considered are in a liquid state and thus have a variable geometry. ...to name some of the
issues encountered. Generally more than one or all of these effects are present at once.
Simulations can in these cases be used to identify distinct footprints of such distortions and thus give the
experimenter a means of deconvoluting them from the signal.
We will present a study of this kind along with the newest developments of the McXtrace software package.
Understanding the distributions of strain within solid bodies undergoing plastic deformations has been of interest for many years in a wide range of disciplines, ranging from basic materials science to biology. However, the desire to investigate these strain fields has been frustrated by the inaccessibility of the interior of most samples to detailed investigation without destroying the sample in the process. To some extent, this has been remedied by the development of advanced surface measurement techniques as well as computer models based on Finite Element methods. Over the last decade, this situation has changed by the introduction of a range of tomographic methods based both on advances in computer technology and in instrumentation, advances which have opened up the interior of optically opaque samples for detailed investigations. We present a general method for assessing the strain in the interior of marker-containing specimens undergoing various types of deformation. The results are compared with Finite Element modelling.
A method for non-destructive characterization of plastic deformation in bulk materials is presented. The method is based on X-ray absorption microtomography investigations using X-rays from a synchrotron source. The method can be applied to materials that contain marker particles, which have an atomic number significantly different from that of the matrix material. Data were acquired at the dedicated microtomography instrument at beamline BW2 at HASYLAB/DESY, for a cylindrical aluminium sample containing W particles with an average particle diameter of 7 μm. The minimum detectable size of the maker particles is 1-2 μm with the present spatial resolution at HASYLAB. The position (x,y,z) of all the detected marker particles within 1 mm3 was determined as function of strain. The sample was deformed in stepwise compression along the axis of the cylinder. A tomographic scan was performed after each deformation step. After a series of image analysis steps to identify the centre of mass of individual particles and alignment of the successive tomographic reconstructions, the displacements of individual particles could be tracked as a function of external strain. The particle displacements are then used to identify local displacement gradient components, from which the local 3D plastic strain tensor can be determined. This allows us to map the strain components as a function of location inside a deforming metallic solid.