X-ray Computed Tomography Data Research Articles

Topological analysis of X-ray CT data for the recognition and trending of subtle changes in microstructure under material aging

X-ray computed tomography (CT) is an established non-destructive tool for 3D imaging of multiphasic composites. Numerous applications of X-ray CT in medical diagnosis and materials characterization have been reported, many involving field-specific innovations in the imaging technology itself. Yet, quantitative summarization to link image features to properties of interest has been rare. We address this issue by employing state-of-the-art technics in scalar field topology for the summarization of X-ray CT images of an example biphasic system. By varying processing-parameters we create different microstructures, evolve them through accelerated thermal aging, CT-image them pre- and post-aged, and demonstrate the ability of our image summarization method to systematically track process- and age-related changes, which can often be very subtle. A novel aspect of the algorithm involves recognition over multiple resolution levels, which provides deeper insight into the pattern relationship between grain-like features and their neighbors. The method is general, adaptable to diverse image reconstruction methods and materials systems, and particularly useful in applications where practical constraints on the sample-size limits the reliable use of more complex models, e.g., convolutional neural networks.

Deep Learning-based Inaccuracy Compensation in Reconstruction of High Resolution XCT Data

While X-ray computed tomography (XCT) is pushed further into the micro- and nanoscale, the limitations of various tool components and object motion become more apparent. For high-resolution XCT, it is necessary but practically difficult to align these tool components with sub-micron precision. The aim is to develop a novel reconstruction methodology that considers unavoidable misalignment and object motion during the data acquisition in order to obtain high-quality three-dimensional images and that is applicable for data recovery from incomplete datasets. A reconstruction software empowered by sophisticated correction modules that autonomously estimates and compensates artefacts using gradient descent and deep learning algorithms has been developed and applied. For motion estimation, a novel computer vision methodology coupled with a deep convolutional neural network approach provides estimates for the object motion by tracking features throughout the adjacent projections. The model is trained using the forward projections of simulated phantoms that consist of several simple geometrical features such as sphere, triangle and rectangular. The feature maps extracted by a neural network are used to detect and to classify features done by a support vector machine. For missing data recovery, a novel deep convolutional neural network is used to infer high-quality reconstruction data from incomplete sets of projections. The forward and back projections of simulated geometric shapes from a range of angular ranges are used to train the model. The model is able to learn the angular dependency based on a limited angle coverage and to propose a new set of projections to suppress artefacts. High-quality three-dimensional images demonstrate that it is possible to effectively suppress artefacts caused by thermomechanical instability of tool components and objects resulting in motion, by center of rotation misalignment and by inaccuracy in the detector position without additional computational efforts. Data recovery from incomplete sets of projections result in directly corrected projections instead of suppressing artefacts in the final reconstructed images. The proposed methodology has been proven and is demonstrated for a ball bearing sample. The reconstruction results are compared to prior corrections and benchmarked with a commercially available reconstruction software. Compared to conventional approaches in XCT imaging and data analysis, the proposed methodology for the generation of high-quality three-dimensional X-ray images is fully autonomous. The methodology presented here has been proven for high-resolution micro-XCT and nano-XCT, however, is applicable for all length scales.

Segmenting Water Observed with X-Ray Computed Tomography in Gas Diffusion Layers of Polymer Electrolyte Fuel Cells Using Convolutional Neural Networks

Gas diffusion layers (GDLs) are responsible for oxygen delivery to the catalyst layer and water management in polymer electrolyte fuel cells (PEFCs). With x-ray computed tomography (CT) – a non-destructive technique, one can observe water transport through the GDL, and also assess local wettability. With ability of X-ray CT to capture dynamics of water transport the quantity of data and the time required to process the data increases. There is a need to further automate data processing and analysis through machine learning; in particular, convolutional neural networks (CNNs). CNNs can be applied to a multitude of image processing tasks such as finding centers of rotation for CT data [1], removal of reconstruction artifacts [1], and denoising of data sets [2]. However, CNNs are probably most known for their ability to perform segmentation/labeling.Although CNNs have proven to be widely applicable to image processing and analysis, it has also become apparent that individual pairings of architecture and application must be validated [1]. As such, we focus on the use of CNNs to segment synchrotron X-ray CT data sets for water within various GDLs. The water is tracked for an in-situ evaporation/condensation experiment. Knowledge of the water's location and shape as a function of time is necessary to understand local GDL’s wettability and its effect on PEFC performance. The challenge these samples present to more traditional segmentation methods is the low contrast between water and carbon fibers. Depending on the composition of a GDL, binder, polytetrafluoroethylene (PTFE), and the microporous layer (MPL) may also be present in the same narrow window of gray-scale values. Separating these phases is essential in evaluating the material's water transport properties. We will present results from both our efforts with CNNs and the specific PEFC experiment that produced the data sets.

Conserved in-ovo cranial ossification sequences of extant saurians allow estimation of embryonic dinosaur developmental stages

Dinosaur embryos are among the rarest of fossils, yet they provide a unique window into the palaeobiology of these animals. Estimating the developmental stage of dinosaur embryos is hindered by the lack of a quantitative method for age determination, by the scarcity of material, and by the difficulty in visualizing that material. Here we present the results of a broad inquiry, using 3D reconstructions from X-ray computed tomography data, into cranial ossification sequences in extant saurian taxa and in well-preserved embryos of the early branching sauropodomorph dinosaur Massospondylus carinatus. Our findings support deep-time conservation of cranial ossification sequences in saurians including dinosaurs, allowing us to develop a new method for estimating the relative developmental percentage of embryos from that clade. We also observe null-generation teeth in the Massospondylus carinatus embryos which get resorbed or shed before hatching, similar to those of geckos. These lines of evidence allow us to confidently estimate that the Massospondylus carinatus embryos are only approximately 60% through their incubation period, much younger than previously hypothesized. The overall consistency of our results with those of living saurians indicates that they can be generalized to other extinct members of that lineage, and therefore our method provides an independent means of assessing the developmental stage of extinct, in-ovo saurians.

Refine reconstruction and verification of meso-scale modeling of three-dimensional five-directional braided composites from X-ray computed tomography data

This paper proposes a statistical approach to generate the refine meso-scale representative volume element of three-dimensional (3D) five-directional braided composites considering the fabric compaction and the axial yarn torsion. The method firstly extracts the regions of interest to obtain the reference model (R-model) from the X-ray computed tomography (Micro-CT) data. Then, the geometric parameters of the collected yarns of R-model are statistically analyzed based on the scanning tomograms. Consequently, the meso-scale statistical model (S-model) are reconstructed after being fitted with the appropriate function. Moreover, for the validation of S-model, the traditional ideal model (I-model) and the experimental tests are considered. The results show that the proposed S-model is capable of accurately presenting the effects of the fabric compaction and yarn torsion on the mechanical behaviors of 3D braided composites, validated by the elastic constants and the damaged modes.

Numerical investigation of the impact of washcoat distribution on the filtration performance of gasoline particulate filters

A three-step numerical model sheds light on the impact of three-way catalyst washcoat distribution within the cordierite porous wall of a clean gasoline particulate filter on its filtration performance. The model relies on (1) the numerical reconstruction of porous wall sections with various washcoat distributions and coating amounts, generated using a novel set of morphological image processing operations applied on segmented X-ray computed tomography data, (2) the computation of the flow field using the lattice Boltzmann method, and (3) the prediction of soot capture efficiency by solving the Langevin equation. The impact of washcoat distribution and amount on the pressure drop, permeability, filtration efficiency, and filter quality factor is systematically investigated. For a non-uniform washcoat distribution, an unexpected decrease in filtration efficiency with an increase in washcoat amount is explained and this highlights the complexity of the effects generated by the deposition of washcoat within the porous wall of the filter.

Linear Elastic FE-Analysis of Porous, Laser Welded, Heat Treatable, Aluminium High Pressure Die Castings Based on X-Ray Computed Tomography Data.

The welding of aluminium high pressure die castings is a well known and broadly investigated challenge in various fields of industry and research. Prior research in this specific field mainly focused on the optimisation of the welding and the casting process and on the cause of the frequently occurring porosity and incomplete fusion phenomena, whereas the impacts of these defects have hardly been addressed. Therefore, the underlying study presents the investigation of weldments in EN AC-AlSi10MnMg high pressure aluminium die castings by linear elastic finite element analysis based on X-ray computed tomography as a novel approach. Hereby, four laser weldments with differing surfaces and pore contents were investigated by X-ray computed tomography and tensile testing. Based on the voxel datasets of the porous weldments, triangular finite element meshes were generated and a numerical finite element analysis was conducted. Good agreement of the stress–strain curves between the simulations and the experiments was achieved.

Structural and Morphological Quantitative 3D Characterisation of Ammonium Nitrate Prills by X-Ray Computed Tomography.

The mixture of ammonium nitrate (AN) prills and fuel oil (FO), usually referred to as ANFO, is extensively used in the mining industry as a bulk explosive. One of the major performance predictors of ANFO mixtures is the fuel oil retention, which is itself governed by the complex pore structure of the AN prills. In this study, we present how X-ray computed tomography (XCT), and the associated advanced data processing workflow, can be used to fully characterise the structure and morphology of AN prills. We show that structural parameters such as volume fraction of the different phases and morphological parameters such as specific surface area and shape factor can be reliably extracted from the XCT data, and that there is a good agreement with the measured oil retention values. Importantly, oil retention measurements (qualifying the efficiency of ANFO as explosives) correlate well with the specific surface area determined by XCT. XCT can therefore be employed non-destructively; it can accurately evaluate and characterise porosity in ammonium nitrate prills, and even predict their efficiency.

Thermal wave interferometry measurements and microstructural analysis of twin wire arc spray cylinder coatings for passenger car engines

With intent to increase the efficiency of combustion engines in passenger cars, thermally sprayed cylinder bore coatings replaced cast iron liners as cylinder running surfaces over the past years. Since the functionality of an engine depends on the life-long integrity of these coatings only a few 100μm thick, monitoring their physical properties in series production is most important. This contribution focuses on the non-destructive testing of thermally sprayed coatings by thermal wave interferometry (TWI), investigating the relationship between thermal and microstructural properties. Microstructural characteristics such as laterally expanded splat interfaces or spherical pores correlate with macroscopic quality criteria such as adhesion, cohesion, or corrosion resistance. Further on, especially splat interfaces act as thermal barriers within the coatings. Hence, TWI thermal diffusivity measurements were compared to optical microscopy images of polished cross sections and three-dimensional X-ray computed tomography data at multiple positions in a crankcase. Both, microscopy images as well as computed tomography data were analyzed quantitatively by image processing algorithms. Due to this, local variations in thermal diffusivity were found, which revealed a significant correlation with microstructural characteristics. Further on, a significant influence of the amount, size, and shape of microstructural volume defects on the local thermal diffusivity of thermally sprayed coatings is carried out in this contribution.

A Study on Factors Influencing the Accuracy Evaluation of Dimensional X-Ray Computed Tomography with Multi-sphere Standards

Industrial X-ray Computed Tomography (XCT) is one of the available choices for measuring parts’ internal and external geometries. However, there are still several challenges that limit the current XCT technologies from being widely spread as a dimensional metrology tool. In the study presented in this paper, multi-sphere standards were developed and used for the evaluation of the accuracy of dimensional measurements performed with an open-ended 450 kV XCT system of 0.4 mm focal spot. Normally, the multi-sphere standard refers to a combination of several ball rods of various heights, where a ‘ball rod’ is understood as a ruby sphere mounted on a rod that is usually made of carbon fiber or ceramic. The main purpose was to determine if the rod’s material, the spatial distribution of the ruby spheres, and/or the selection of test lengths (i.e., the center-to-center distance between two ruby spheres) influence the determination of the maximum permissible sphere distance error (MPESD) attributable to XCT data. The experimental data suggested ceramic is more suitable as a rod material than carbon fiber, and also, the spatial distribution of the ruby spheres has no obvious influence on the accuracy evaluation of dimensional measurements. However, the selection of test lengths will significantly affect the accuracy evaluation results. In particular, the results of dimensional measurements showed that the evaluation of the MPESD with multi-sphere standards for the XCT system used in this study can be expressed as MPESD = ± (159 + L/800) μm.

New method for analysis of X-ray computed tomography scans of TRISO fuel forms

X-ray computed tomography (XCT) is a powerful characterization tool that has been used for examination of both tristructural-isotropic (TRISO) coated particles and fuel forms. Rapid, nondestructive imaging of complete specimens by XCT provides significant characterization advantages in both speed and comprehensiveness over traditional materialographic methods. This paper discusses a method that has been developed to analyze XCT images of pebble fuel forms containing ~18,000 TRISO particles each. The XCT images are three-dimensional with contrast based on relative x-ray transparency. In the case of TRISO fuel forms, XCT image intensity distinguishes between the high-density kernel, the medium-density SiC, and the low-density matrix and carbon layers. Image analysis software has been developed to process XCT data sets to extract spatial distribution information, including particle nearest-neighbor distances, local packing fraction, and fuel-free zone thickness in TRISO fuel forms. Although this type of high-resolution XCT analysis is likely not feasible as a quality control measurement for commercial TRISO fuel, it is highly useful in generating feedback for the development of fuel form fabrication processes.

Simultaneous estimation of in situ porosity and thermal structure from core sample measurements and resistivity log data at Nankai accretionary prism

The shallow accretionary prism of the Nankai Trough is a location where both large interplate earthquakes and slow earthquakes occur. Since the physical properties of sedimentary materials are important topics for understanding the structure of the prism, numerous ocean drilling expeditions have been conducted in that region to obtain logging data and core samples. Although the physical properties of the obtained samples are normally measured onboard immediately after coring, estimations of in situ physical properties are difficult because of differences in laboratory and in situ physical conditions. Herein, we propose a new method for estimating in situ porosity from downhole electrical resistivity log data that evaluates in situ porosity and thermal structure simultaneously using correlations between the porosity and resistivity, and between the porosity and thermal conductivity that were established based on laboratory measurements. When constructing physical property correlations, X-ray computed tomography data play an important role in estimating the porosity of samples from which resistivity or thermal conductivity were measured. To validate our method, we compared the estimation with density log data collected at Site C0002 and found that the estimated in situ porosity shows good agreement with the in situ porosity converted from density log data. A comparison with porosity measured onboard for core and cutting samples showed that they are consistent with each other. With this new method, continuous distributions of in situ porosity and thermal structure can be estimated simultaneously based on resistivity log data and heat flow, which are basic quantities acquired during ocean drilling science expeditions.

A cone-beam X-ray computed tomography data collection designed for machine learning

Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction. Forty-two walnuts were scanned with a laboratory X-ray set-up to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different source orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation.

An interactive and intuitive visualisation method for X-ray computed tomography data of biological samples in 3D Portable Document Format

3D imaging approaches based on X-ray microcomputed tomography (microCT) have become increasingly accessible with advancements in methods, instruments and expertise. The synergy of material and life sciences has impacted biomedical research by proposing new tools for investigation. However, data sharing remains challenging as microCT files are usually in the range of gigabytes and require specific and expensive software for rendering and interpretation. Here, we provide an advanced method for visualisation and interpretation of microCT data with small file formats, readable on all operating systems, using freely available Portable Document Format (PDF) software. Our method is based on the conversion of volumetric data into interactive 3D PDF, allowing rotation, movement, magnification and setting modifications of objects, thus providing an intuitive approach to analyse structures in a 3D context. We describe the complete pipeline from data acquisition, data processing and compression, to 3D PDF formatting on an example of craniofacial anatomical morphology in the mouse embryo. Our procedure is widely applicable in biological research and can be used as a framework to analyse volumetric data from any research field relying on 3D rendering and CT-biomedical imaging.

Coupled ptychography and tomography algorithm improves reconstruction of experimental data

Three-dimensional (3D) x-ray microscopy by ptychographic tomography requires elaborate numerical reconstructions. We describe a coupled ptychography-tomography reconstruction algorithm and apply it to an experimental ptychographic x-ray computed tomography data set of a catalyst particle. Compared to the traditional sequential algorithm, in which ptychographic projections are reconstructed to serve as input for subsequent tomographic reconstruction, the coupled ptychography-tomography algorithm reconstructs the 3D volume with higher spatial resolution over a larger field of view. Coupling the data from different projections improves the overall reconstruction, and the ptychographic sampling in individual projections can be coarsened beyond the point of overlap between neighboring scan points, still leading to stable reconstructions.

Discontinuous deformation analysis for SH-body

Based on micro X-ray computed tomography data, the spherical harmonic (SH) function can be employed to reconstruct the nature sand particle, referred to as SH-body. Thus, the sand particle micromorphology can be described by analytic functions. Thanks to these analytic functions, the closest points between SH-bodies for contact detection can be identified by using a second-order geometric iteration algorithm. Meanwhile, the equivalent variational inequality is formulated to determine the inter-particle contact forces for the three-dimensional cases. To capture the kinematics and kinetics of SH-bodies, this study deduces discontinuous deformation analysis (DDA) formula based on the principle of minimum potential energy. The study establishes a framework for simulating the dynamic behavior of SH-bodies, and the proposed methodology is validated through numerical examples.

Pressure Drop in High-Density Porous Metals via Tomography Datasets

This study combines three-dimensional advanced imaging techniques and computational fluid dynamic modelling and simulation (CFD) to characterise the pressure drop of flowing fluid across high-density porous metals utilising high-resolution X-ray computed tomography data. The modelling approach quantifies the combined effects of pore volume fraction, pore connectivity, pore size and morphology on the flow behaviour of porous metals and to study in more detail the pressure drop behaviour characterised by the sudden change in pore volume by stacking of differential porous samples at the pore-level. The resulting predicted values of the pressure drop as a function of superficial fluid velocity ranging from Darcy to turbulent fluid flow regimes were used to account for the permeability (k0) and Form drag coefficient (C) of these materials. Supportable agreement between CFD modelled data against empirical measurements available in the literature was substantiated. Therefore it is considered that this approach could lead practically to minimizing the number of design iterations required for the processing of novel-attributing porous metallic materials for applications involving fluid flow.

A Tool to Generate Grain-Resolved Open-Cell Metal Foam Models

The development and use cases of an open-source filter for DREAM.3D that instantiates synthetic, grain-resolved, open-cell metal foam volumes are presented. The new capability allows for both synthetic-grain overlay of X-ray computed tomography data as well as fully synthetic foam geometry and grains. For the latter, a novel technique using Euclidean distances instantiates the 3D open-cell foam morphology, enabling user control of pore size, strut cross-section shape, and strut thickness variability. By integrating this approach into the DREAM.3D architecture, the entire DREAM.3D suite of filters is immediately available; thus, enabling both user control and quantification of grain size, shape, and crystallographic orientation statistics (among other metrics) as well as meshing algorithms to enable subsequent numerical analysis.

Measurement of Damköhler number on the X-Ray computed tomography basis in the process of heavy oil carbonate reservoir acidizing

Nowadays, cases of the joint use of thermal and chemical methods of enhanced oil recovery have become more frequent. In this regard, acid treatment has an important role, which allows increasing the productivity of wells with low-permeability carbonate reservoirs. At the experimental level, the presence of an optimal injection mode has been proven, that depends on the dimensionless of Damköhler number and leads to the formation of “wormholes” of dissolution with increasing reservoir permeability. In this work, a new optimized method for calculating the Damköhler number based on X-ray computed tomography data is proposed. A comparison of the calculation of values by the classical and the new technique for different wormholes is presented.

Ring artifact suppression in X-ray computed tomography using a simple, pixel-wise response correction.

We present a pixel-specific, measurement-driven correction that effectively reduces errors in detector response that give rise to the ring artifacts commonly seen in X-ray computed tomography (CT) scans. This correction is easy to implement, suppresses CT artifacts significantly, and is effective enough for use with both absorption and phase contrast imaging. It can be used as a standalone correction or in conjunction with existing ring artifact removal algorithms to further improve image quality. We validate this method using two X-ray CT data sets acquired using monochromatic sources, showing post-correction signal-to-noise increases of up to 55%, and we define an image quality metric to use specifically for the assessment of ring artifact suppression.