Caori Organista

Bio: Caori Organista is an academic researcher from Paul Scherrer Institute. The author has contributed to research in topics: Optics & Visibility. The author has an hindex of 1, co-authored 1 publications receiving 3 citations. Previous affiliations of Caori Organista include ETH Zurich & Ghent University.

Tunable X-ray dark-field imaging for sub-resolution feature size quantification in porous media.

Abstract: X-ray computed micro-tomography typically involves a trade-off between sample size and resolution, complicating the study at a micrometer scale of representative volumes of materials with broad feature size distributions (e.g. natural stones). X-ray dark-field tomography exploits scattering to probe sub-resolution features, promising to overcome this trade-off. In this work, we present a quantification method for sub-resolution feature sizes using dark-field tomograms obtained by tuning the autocorrelation length of a Talbot grating interferometer. Alumina particles with different nominal pore sizes (50 nm and 150 nm) were mixed and imaged at the TOMCAT beamline of the SLS synchrotron (PSI) at eighteen correlation lengths, covering the pore size range. The different particles cannot be distinguished by traditional absorption µCT due to their very similar density and the pores being unresolved at typical image resolutions. Nevertheless, by exploiting the scattering behavior of the samples, the proposed analysis method allowed to quantify the nominal pore sizes of individual particles. The robustness of this quantification was proven by reproducing the experiment with solid samples of alumina, and alumina particles that were kept separated. Our findings demonstrate the possibility to calibrate dark-field image analysis to quantify sub-resolution feature sizes, allowing multi-scale analyses of heterogeneous materials without subsampling.

Optimization of the visibility of a tunabledual-phase x-ray grating interferometer

Abstract: Dual-phase x-ray grating interferometry (DP-XGI) is a recently developed imaging technique that can retrieve structural information in the sub-micro scale over areas in the millimeter range. This is performed by use of the scattering signal, which is sensitive to structures that lie below the intrinsic spatial resolution of the imaging system. A quantitative understanding of the microstructure is possible when the scattering signal is retrieved within a range of auto-correlation lengths of the features of interest. High visibility of fringes in this length range is desirable, but no straightforward framework exists for choosing design parameters of the imaging system for such optimization. The purpose of this work is to present an optimization protocol for DP-XGI based on a Fresnel propagation simulation framework which evaluates different parameters of the optical system, utilizing the mean visibility of the fringes at the detector plane as a figure of merit to optimize the DP-XGI for a conventional lab x-ray source. The performance of the numerical simulation with realistic component parameters is validated with the experimental results obtained at a lab-based setup. The results of the validation confirm the robustness of the model for the evaluation of the different components of the interferometer and its optimization at low and high energies.

Detailed analysis of the interference patterns measured in lab-based X-ray dual-phase grating interferometry through wave propagation simulation.

Abstract: In this work, we analyze the interference patterns measured in lab-based dual-phase grating interferometry and for the first time explain the spatial dependencies of the measured interference patterns and the large visibility deviations between the theoretical prediction and the experimental results. To achieve this, a simulator based on wave propagation is developed. This work proves that the experimental results can be simulated with high accuracy by including the effective grating thickness profile induced by the cone-beam geometry, the measured detector response function and a non-ideal grating shape. With the comprehensive understanding of dual-phase grating interferometry, this provides the foundations for a more efficient and accurate algorithm to retrieve sample's structure information, and the realistic simulator is a useful tool for optimizing the set-up.

Exploration of the X-ray Dark-Field Signal in Mineral Building Materials

Abstract: Mineral building materials suffer from weathering processes such as salt efflorescence, freeze–thaw cycling, and microbial colonization. All of these processes are linked to water (liquid and vapor) in the pore space. The degree of damage following these processes is heavily influenced by pore space properties such as porosity, pore size distribution, and pore connectivity. X-ray computed micro-tomography (µCT) has proven to be a valuable tool to non-destructively investigate the pore space of stone samples in 3D. However, a trade-off between the resolution and field-of-view often impedes reliable conclusions on the material’s properties. X-ray dark-field imaging (DFI) is based on the scattering of X-rays by sub-voxel-sized features, and as such, provides information on the sample complementary to that obtained using conventional µCT. In this manuscript, we apply X-ray dark-field tomography for the first time on four mineral building materials (quartzite, fired clay brick, fired clay roof tile, and carbonated mineral building material), and investigate which information the dark-field signal entails on the sub-resolution space of the sample. Dark-field tomography at multiple length scale sensitivities was performed at the TOMCAT beamline of the Swiss Light Source (Villigen, Switzerland) using a Talbot grating interferometer. The complementary information of the dark-field modality is most clear in the fired clay brick and roof tile; quartz grains that are almost indistinguishable in the conventional µCT scan are clearly visible in the dark-field owing to their low dark-field signal (homogenous sub-voxel structure), whereas the microporous bulk mass has a high dark-field signal. Large (resolved) pores on the other hand, which are clearly visible in the absorption dataset, are almost invisible in the dark-field modality because they are overprinted with dark-field signal originating from the bulk mass. The experiments also showed how the dark-field signal from a feature depends on the length scale sensitivity, which is set by moving the sample with respect to the grating interferometer.

General solution for quantitative dark-field contrast imaging with grating interferometers

Abstract: Dark-field contrast imaging with grating interferometers has proven to hold huge potential for numerous applications with X-rays and with neutrons conveying biology and medicine as well as engineering and magnetism, respectively. However, a concept to extract quantitative information is still missing. Here a general theory as well as a measurement strategy is introduced, allowing extraction of quantitative small-angle scattering information such as structure sizes and scattering cross sections. The validity of the description is demonstrated by a specific example from literature.

Deep Learning of Multiresolution X-Ray Micro-Computed-Tomography Images for Multiscale Modeling

Abstract: Field-of-view and resolution trade-offs in x-ray micro-computed-tomography (micro-CT) imaging limit the characterization, analysis, and model development of multiscale porous systems. To this end, we develop an applied methodology utilizing deep learning to enhance low-resolution (LR) images over large sample sizes and create multiscale models capable of accurately simulating experimental fluid dynamics from the pore (microns) to continuum (centimeters) scale. We develop a three-dimensional (3D) enhanced deep-superresolution (EDSR) convolutional neural network to create superresolution (SR) images from LR images, which alleviates common micro-CT hardware and/or reconstruction defects in high-resolution (HR) images. When paired with pore-network simulations and parallel computation, we can create large 3D continuum-scale models with spatially varying flow and material properties. We quantitatively validate the workflow at various scales using direct HR and SR image similarity, pore-scale material and/or flow simulations, and continuum-scale multiphase-flow experiments (drainage-immiscible flow pressures and 3D fluid-volume fractions). The SR images and models are comparable to the HR ground truth and generally accurate to within experimental uncertainty at the continuum scale across a range of flow rates. They are found to be significantly more accurate than their LR counterparts, especially in cases where a wide distribution of pore sizes are encountered. The applied methodology opens up the possibility to image, model, and analyze truly multiscale heterogeneous systems that are otherwise intractable.

3D Visualization of mineral carbonation : μCT as a tool to understand pore scale processes

Abstract: A material can only be considered as a waste if it has no economic value. Transforming waste products, that would normally end up in landfills, into raw materials and products that have economic value is one of the main goals in a zero waste mindset and one of the possibilities to ensure a sustainable future. This thesis focuses on a valorization route for steel slag and CO2, two substances which are generally considered as waste products. When a compact of fine grained steel slag grains is exposed to pressurized CO2, a carbonation reaction is triggered, which binds the grains together and allows to generate building blocks with a high compressive strength. The reactions associated with the carbonation process are an interplay between CO2, water and minerals and take place in the pore space of the steel slag compact. The main focus of this work is to analyse the carbonation

Optimization of the visibility of a tunabledual-phase x-ray grating interferometer

Abstract: Dual-phase x-ray grating interferometry (DP-XGI) is a recently developed imaging technique that can retrieve structural information in the sub-micro scale over areas in the millimeter range. This is performed by use of the scattering signal, which is sensitive to structures that lie below the intrinsic spatial resolution of the imaging system. A quantitative understanding of the microstructure is possible when the scattering signal is retrieved within a range of auto-correlation lengths of the features of interest. High visibility of fringes in this length range is desirable, but no straightforward framework exists for choosing design parameters of the imaging system for such optimization. The purpose of this work is to present an optimization protocol for DP-XGI based on a Fresnel propagation simulation framework which evaluates different parameters of the optical system, utilizing the mean visibility of the fringes at the detector plane as a figure of merit to optimize the DP-XGI for a conventional lab x-ray source. The performance of the numerical simulation with realistic component parameters is validated with the experimental results obtained at a lab-based setup. The results of the validation confirm the robustness of the model for the evaluation of the different components of the interferometer and its optimization at low and high energies.

Predictive Soft Computing Methods for Building Digital Rock Models Verified by Positron Emission Tomography Experiments

Abstract: The influence of core-scale heterogeneity on continuum-scale flow and laboratory measurements are not well understood. To address this issue, we propose a fully automated workflow based on soft computing to characterize the heterogeneous flow properties of cores for predictive continuum-scale models. While the proposed AI-based workflow inherently has no trained knowledge of rock petrophysical properties, our results demonstrate that image features and morphological properties provide sufficient measures for petrophysical classification. Micro X-ray computed tomography (μxCT) image features were extracted from full core plug images by using a Convolutional Neural Network and Minkowski functional measurements. The features were then classified into specific classes using Principal Component Analysis followed by K-means clustering. Next, the petrophysical properties of each class were evaluated using pore-scale simulations to substantiate that unique classes were identified. The μxCT image was then up-scaled to a continuum-scale grid based on the defined classes. Last, simulation results were evaluated against real-time flooding data monitored by Positron Emission Tomography. Both homogeneous sandstone and heterogeneous carbonate were tested. Simulation and experimental saturation profiles compared well, demonstrating that the workflow provided high-fidelity characterization. Overall, we provided a novel workflow to build digital rock models in a fully automated way to better understand the impacts of heterogeneity on flow.