Synchrotron-based X-ray tomography offers the potential for rapid large-scale reconstructions of the interiors of materials and biological tissue at fine resolution. However, for radiation sensitive samples, there remain fundamental trade-offs between damaging samples during longer acquisition times and reducing signals with shorter acquisition times. We present a deep convolutional neural network (CNN) method that increases the acquired X-ray tomographic signal by at least a factor of 10 during low-dose fast acquisition by improving the quality of recorded projections. Short-exposure-time projections enhanced with CNNs show signal-to-noise ratios similar to long-exposure-time projections. They also show lower noise and more structural information than low-dose short-exposure acquisitions post-processed by other techniques. We evaluated this approach using simulated samples and further validated it with experimental data from radiation sensitive mouse brains acquired in a tomographic setting with transmission X-ray microscopy. We demonstrate that automated algorithms can reliably trace brain structures in low-dose datasets enhanced with CNN. This method can be applied to other tomographic or scanning based X-ray imaging techniques and has great potential for studying faster dynamics in specimens
Nucleation of barite (BaSO4) has broad implications in geological, environmental, and materials sciences. While impurity metals are common, our understanding of how they impact nucleation remains dim. Here, we used classical optical microscopy compared to fast X-ray nanotomography (XnT) to investigate heterogeneous nucleation of barite on silica in situ with Sr2+ as an impurity ion. The observed barite nucleation rates were consistent with classical nucleation theory (CNT), where barite crystals displayed a nonuniform size distribution, exhibiting distinct morphologies and incubation periods in Sr-free solutions. While undetectable with optical microscopy, nanotomography revealed that addition of Sr2+ enhances nucleation rates driven by the pre-factor in CNT, likely because both adsorbed Ba2+ and Sr2+ act as precursor sites on which nucleation occurs. Sr2+ simultaneously inhibits growth, however, leading to a homogeneous distribution of smaller crystals. This finding will enable an improved predictive understanding of nucleation in natural and synthetic environments.
This paper presents a deep learning algorithm for tomographic reconstruction (GANrec). The algorithm uses a generative adversarial network (GAN) to solve the inverse of the Radon transform directly. It works for independent sinograms without additional training steps. The GAN has been developed to fit the input sinogram with the model sinogram generated from the predicted reconstruction. Good quality reconstructions can be obtained during the minimization of the fitting errors. The reconstruction is a self‐training procedure based on the physics model, instead of on training data. The algorithm showed significant improvements in the reconstruction accuracy, especially for missing‐wedge tomography acquired at less than 180° rotational range. It was also validated by reconstructing a missing‐wedge X‐ray ptychographic tomography (PXCT) data set of a macroporous zeolite particle, for which only 51 projections over 70° could be collected. The GANrec recovered the 3D pore structure with reasonable quality for further analysis. This reconstruction concept can work universally for most of the ill‐posed inverse problems if the forward model is well defined, such as phase retrieval of in‐line phase‐contrast imaging.
This paper presents an algorithm to calibrate the center-of-rotation for X-ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential for reducing or removing other artifacts caused by instrument instability, detector non-linearity, etc. An open-source toolbox, which integrates the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided.
Modern day tomographs enable the research community to investigate the internal flow behaviour of a fluidized bed by non invasive methods that partially overcome the opaque nature of a dense bubbling bed. Each tomographic modality has its own limitations and advantages and in the present study two modern day tomographic systems were evaluated with respect to their performance on a cold dense fluidized bed. The two tomographs investigated are an Electrical Capacitance Tomography (ECT) tomograph and a time-resolved X-ray tomography tomograph. The study was performed on spherical glass particles with various particle size distributions that could mainly be classified as Geldart B or D particles. Two experimental towers were employed, one with a diameter of 10.4cm and the other 23.8cm while compressed air was used as fluidizing fluid during all of the experiments.Results obtained with both systems are provided in comprehensive figures and tables and some first results are obtained with the time-resolved X-ray tomography system. The bubble size measurements of both tomographs are compared with several theoretical correlations via the root mean square error of the predictions (RMSEP). With the results it was also concluded that a small amount of small particles can noticeably alter the fluidization hydrodynamics of a powder. The bubble frequencies are also presented to aid in understanding the hydrodynamic behaviour of the powders investigated. A comprehensive summary of the two tomographic modalities is also provided.
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