Wissenschaftliche Artikel der BAM
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- Computed tomography (24) (entfernen)
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Analyzing damages at concrete structures due to physical, chemical, and mechanical exposures need the application of innovative non-destructive testing methods that are able to trace spatial changes of microstructures. Here, the utility of three different crack detection methods for the analysis of computed tomograms of various cementitious building materials is evaluated. Due to the lack of reference samples and standardized image quality evaluation procedures, the results are compared with manually segmented reference data sets. A specific question is how automatic crack detection can be used for the quantitative characterization of damage processes, such as crack length and volume. The crack detection methods have been integrated into a scientific visualization system that allows displaying the tomography images as well as presenting the results.
Modelling becomes more and more important in modern NDE. It is increasingly used to optimize techniques for complex applications, to support the preparation of written procedures, and for education purposes. To describe the complete chain of RT, the model includes simulating all necessary properties of X- or Gamma-ray sources, the interaction of photons with material with special attention to scattered radiation, the detection process, and the complete geometrical RT setup handling arbitrary parts or constructions. Depending on the given inspection problem and the influencing factors that should be addressed by the simulation, an appropriate physical model has to be chosen to describe the underlying interaction mechanisms. The simulator aRTist combines analytical and Monte Carlo methods to efficiently model the radiation transport such that transmission as well as scatter techniques can be modelled. In this contribution we Focus on Monte Carlo Simulation of scatter contribution within aRTist. Examples for RT/tomographic applications and back-scatter techniques are presented to demonstrate the usability of the presented simulation tool for a broad range of radiological applications.
Confocal micro-X-ray fluorescence (micro-XRF) spectroscopy facilitates three-dimensional (3D) elemental imaging of heterogeneous samples in the micrometer range. Laboratory setups using X-ray tube excitation render the method accessible for diverse research fields but interpretation of results and quantification remain challenging. The attenuation of X-rays in composites depends on the photon energy as well as on the composition and density of the material. For confocal micro-XRF, attenuation severely impacts elemental distribution information, as the signal from deeper layers is distorted by superficial layers. Absorption correction and quantification of fluorescence measurements in heterogeneous composite samples have so far not been reported. Here, an absorption correction approach for confocal micro-XRF combining density information from microcomputed tomography (micro-CT) data with laboratory X-ray absorption spectroscopy (XAS) and synchrotron transmission measurements is presented. The energy dependency of the probing volume is considered during the correction. The methodology is demonstrated on a model composite sample consisting of a bovine tooth with a clinically used restoration material.
Simulation-Assisted Augmentation of Missing Wedge and Region-of-Interest Computed Tomography Data
(2024)
This study reports a strategy to use sophisticated, realistic X-ray Computed Tomography (CT) simulations to reduce Missing Wedge (MW) and Region-of-Interest (RoI) artifacts in FBP (Filtered Back-Projection) reconstructions. A 3D model of the object is used to simulate the projections that include the missing information inside the MW and outside the RoI. Such information augments the experimental projections, thereby drastically improving the reconstruction results. An X-ray CT dataset of a selected object is modified to mimic various degrees of RoI and MW problems. The results are evaluated in comparison to a standard FBP reconstruction of the complete dataset. In all cases, the reconstruction quality is significantly improved. Small inclusions present in the scanned object are better localized and quantified. The proposed method has the potential to improve the results of any CT reconstruction algorithm.