Revealing the microstructural stability of a three-phase soft solid (ice cream) by 4D synchrotron X-ray tomography

Understanding the microstructural stability of soft solids is key to optimizing formulations and processing parameters to improve the materials' properties. In this study, in situ synchrotron X-ray tomography is used to determine the temperature dependence of ice-cream's microstructural evolution, together with the underlying physical mechanisms that control microstructural stability. A new tomographic data processing method was developed, enabling the features to be segmented and quantified. The time-resolved results revealed that the melting-recrystallization mechanism is responsible for the evolution of ice crystal size and morphology during thermal cycling between −15 and −5 °C, while coalescence of air cells is the dominant coarsening mechanism controlling air bubble size and interconnectivity. This work also revealed other interesting phenomena, including the role of the unfrozen matrix in maintaining the ice cream's microstructural stability and the complex interactions between ice crystals and air structures, e.g. the melting and recrystallization of ice crystals significantly affect the air cell's morphology and the behavior of the unfrozen matrix. The results provide crucial information enhancing the understanding of microstructural evolution in multi-phase multi-state complex foodstuffs and other soft solids

Long-range exciton transport in brightly fluorescent furan/phenylene co-oligomer crystals

The design of light-emitting crystalline organic semiconductors for optoelectronic applications requires a thorough understanding of the singlet exciton transport process. In this study, we show that the singlet exciton diffusion length in a promising semiconductor crystal based on furan/phenylene co-oligomers is 24 nm. To achieve this, we employed the photoluminescence quenching technique using a specially synthesized quencher, which is a long furan/phenylene co-oligomer that was facilely implanted into the host crystal lattice. Extensive Monte-Carlo simulations, exciton-exciton annihilation experiments and numerical modelling fully supported our findings. We further demonstrated the high potential of the furan/phenylene co-oligomer crystals for light-emitting applications by fabricating solution-processed organic light emitting transistors

Regularised ADMM reconstruction algorithm for X-ray CT (ADMM-tomo)

<p>Regularised ADMM-type iterative reconstruction algorithm for X-ray tomographic reconstruction.</p> <p>Software highlights:</p> <ul> <li>Tomographic projection data are simulated without the "inverse crime" using <a href="https://github.com/dkazanc/TomoPhantom">TomoPhantom</a>. Noise and artifacts (zingers, rings) can be modelled and added to data if required.</li> <li>Simulated data reconstructed iteratively using ADMM-type algorithm with multiple "plug-and-play" regularisers from <a href="https://github.com/vais-ral/CCPi-Regularisation-Toolkit">CCPi-RegularisationToolkit</a>.</li> <li>Various projection (2D/3D) geometries are supported.</li> </ul

Sandstone rock tomographic data, i12 beamline, DLS synchrotron

&lt;p&gt;This data compliments a paper &lt;i&gt;Conditional generative adversarial networks for stripe artefact removal in high-resolution X-ray tomography &lt;/i&gt;by D. Kazantsev, L. Beveridge, V. Shanmugasundar and O. Magdysyuk to be published in the &lt;strong&gt;Tomography of Materials and Structures&lt;/strong&gt; journal in 2023 (to be updated). This data alongside the data from &lt;a href="https://zenodo.org/records/1443568#.ZBREbUjP1qN"&gt;here&lt;/a&gt; was used to train the network to remove stripe artefacts.&nbsp;&lt;/p&gt;&lt;p&gt;The code is written in Python and located at: https://github.com/dkazanc/NoStripesNet&lt;/p&gt

Conditional generative adversarial networks for stripe artefact removal in high-resolution X-ray tomography

Tomographic imaging supports a great number of medical and material science applications. The collected projection data usually has different types of imaging artefacts and noise. Various image pre-processing and reconstruction methods are used to obtain volumetric datasets of high quality for further analysis. In order to minimise reconstruction artefacts, one can apply either filtering and/or data completion/inpainting techniques which can recover the data. Deep learning (DL) methods to remove artefacts and noise have been successfully applied in the past. In this paper, we present a novel approach based on conditional generative adversarial networks (cGANs) to remove stripe artefacts. The novelty of the presented technique is in how the training data for DL is extracted from the same tomographic dataset that needs recovery. We also provide new deterministic stripe detection and inpainting algorithms to support the development. The presented methods are compared with other stripe removal algorithms and applied to 3D and 4D high-resolution X-ray data collected at Diamond Light Source synchrotron, UK. The proposed DL method delivers reconstructed images with minimised ring artefacts while being a parameter-free approach. A similar DL strategy can also be applied to remove other types of artefacts in images

CCPi-Regularisation toolkit for computed tomographic image reconstruction with proximal splitting algorithms

Iterative reconstruction algorithms are often needed to help solve ill-posed inverse problems in computed tomography (CT), especially cases when tomographic projection data are corrupt, noisy or angularly undersampled. Model-based iterative methods can be adapted to fit the measurement characteristics of the data (e.g. noise statistics) and expectations regarding the reconstructed object (e.g. morphology). The prior information is usually introduced in the form of a regulariser, making the inversion task well-posed.The CCPi-Regularisation toolkit provides a set of variational regularisers (denoisers) which can be embedded in a plug-and-play fashion into proximal splitting methods for image reconstruction. CCPi-RGL comes with algorithms that can satisfy various prior expectations of the reconstructed object, for example being piecewise-constant or piecewise-smooth in nature. The toolkit is written in C language and exploits parallelism with OpenMP directives and the CUDA API; and is wrapped for the Python and MATLAB environments. This paper introduces the toolkit and gives recommendations for selecting a suitable prior model. Keywords: X-ray CT, Iterative methods, Model-based, Regularisation, Denoising, Primal–dual, Big-dat

TomoPhantom, a software package to generate 2D–4D analytical phantoms for CT image reconstruction algorithm benchmarks

In the field of computerized tomographic imaging, many novel reconstruction techniques are routinely tested using simplistic numerical phantoms, e.g. the well-known Shepp–Logan phantom. These phantoms cannot sufficiently cover the broad spectrum of applications in CT imaging where, for instance, smooth or piecewise-smooth 3D objects are common. TomoPhantom provides quick access to an external library of modular analytical 2D/3D phantoms with temporal extensions. In TomoPhantom, quite complex phantoms can be built using additive combinations of geometrical objects, such as, Gaussians, parabolas, cones, ellipses, rectangles and volumetric extensions of them. Newly designed phantoms are better suited for benchmarking and testing of different image processing techniques. Specifically, tomographic reconstruction algorithms which employ 2D and 3D scanning geometries, can be rigorously analyzed using the software. TomoPhantom also provides a capability of obtaining analytical tomographic projections which further extends the applicability of software towards more realistic, free from the “inverse crime” testing. All core modules of the package are written in the C-OpenMP language and wrappers for Python and MATLAB are provided to enable easy access. Due to C-based multi-threaded implementation, volumetric phantoms of high spatial resolution can be obtained with computational efficiency. Keywords: Phantoms, Tomography, Image reconstruction, Iterative methods, Open-sourc