Tracking capsule activation and crack healing in a microcapsule-based self-healing polymer

Structural polymeric materials incorporating a microencapsulated liquid healing agent demonstrate the ability to autonomously heal cracks. Understanding how an advancing crack interacts with the microcapsules is critical to optimizing performance through tailoring the size, distribution and density of these capsules. For the first time, time-lapse synchrotron X-ray phase contrast computed tomography (CT) has been used to observe in three-dimensions (3D) the dynamic process of crack growth, microcapsule rupture and progressive release of solvent into a crack as it propagates and widens, providing unique insights into the activation and repair process. In this epoxy self-healing material, 150 µm diameter microcapsules within 400 µm of the crack plane are found to rupture and contribute to the healing process, their discharge quantified as a function of crack propagation and distance from the crack plane. Significantly, continued release of solvent takes place to repair the crack as it grows and progressively widens

A non-invasive imaging approach for improved assessments on the construction and the condition of historical knotted-pile carpets

The appraisal of the design and the weaving structure of Islamic knotted-pile carpets can tell plenty about the context in which they were produced, and the identification of signs of deterioration can help to establish their condition. These are often somewhat imprecise and laborious examinations, especially when considering carpets of large dimensions. Analytical methods that support these disciplines urge further exploration so that improved interpretations can be obtained.  An interdisciplinary combination of art history, analytical science and textile conservation aimed, on the one hand, to improve the weaving examination of these complex textile objects – by considering the spin of threads and the ply of yarns; the knot count and density; and the weaving structure of warps, wefts and piles – and on the other, to help their condition assessment – by mapping of damaged areas, old repairs and contaminations. For this purpose, the possibilities and limitations of several non-invasive imaging techniques, namely transmitted, raking or incident visible, ultraviolet (UV) and infrared (IR) illumination through Visual Spectral Comparator (VSC), as well as conventional X-radiography, mammography and (micro) CT scanning, were assessed to support the conventional visual examination of the weaving details and present condition of two 17th-century Safavid knotted-pile carpet fragments.  Observation with NUV and NIR imaging with VSC, as well as CT techniques, offered enriching overviews about weaving characteristics, damaged areas or contaminations that were not easily discernible with the naked eye, thus supporting the conventional visual examination. As a result, detailed digital mappings about the technological structure and the condition of the fragments could be obtained in a relatively efficient and accessible way. Moreover, combining art historical identification of the design with the analysis of the weaving structure confirmed that both carpet fragments are border corners that originally belonged to much larger carpets made in the so-called “Indo-Persian” style. The outcome of this interdisciplinary research brings very useful contributions for future art historical and conservation assessments of historical carpets, and it encourages further exploration of imaging techniques in the examination of other textile objects in museums and private collections

Just-in-time deep learning for real-time X-ray computed tomography

Real-time X-ray tomography pipelines, such as implemented by RECAST3D, compute and visualize tomographic reconstructions in milliseconds, and enable the observation of dynamic experiments in synchrotron beamlines and laboratory scanners. For extending real-time reconstruction by image processing and analysis components, Deep Neural Networks (DNNs) are a promising technology, due to their strong performance and much faster run-times compared to conventional algorithms. DNNs may prevent experiment repetition by simplifying real-time steering and optimization of the ongoing experiment. The main challenge of integrating DNNs into real-time tomography pipelines, however, is that they need to learn their task from representative data before the start of the experiment. In scientific environments, such training data may not exist, and other uncertain and variable factors, such as the set-up configuration, reconstruction parameters, or user interaction, cannot easily be anticipated beforehand, either. To overcome these problems, we developed just-in-time learning, an online DNN training strategy that takes advantage of the spatio-temporal continuity of consecutive reconstructions in the tomographic pipeline. This allows training and deploying comparatively small DNNs during the experiment. We provide software implementations, and study the feasibility and challenges of the approach by training the self-supervised Noise2Inverse denoising task with X-ray data replayed from real-world dynamic experiments

Prototyping X-ray tomographic reconstruction pipelines with FleXbox

Computer Tomography (CT) scanners for research applications are often designed to facilitate flexible acquisition geometries. Making full use of such CT scanners requires advanced reconstruction software that can (i) deal with a broad range of geometrical scanning settings, (ii) allows for customization of processing algorithms, and (iii) has the capability to process large amounts of data. FleXbox is a Python-based tomographic reconstruction toolbox focused on these three functionalities. It is built to bridge the gap between low-level tomographic reconstruction packages (e.g. ASTRA toolbox) and high-level distributed systems (e.g. Livermore Tomography Tools). FleXbox allows to model arbitrary source, detector and object trajectories. The modular architecture of FleXbox allows to design an optimal reconstruction approach for a single CT dataset. When multiple datasets of an object are acquired (either different spatial regions or different snapshots in time), they can be combined into a larger high resolution volume or a time series of volumes. The software allows to then create a computational reconstruction pipeline that can run without user interaction and enables efficient computation on large-scale 3D volumes on a single workstation

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

Dating and provenancing the <i>Woman with lantern</i> sculpture – A contribution towards attribution of Netherlandish art

Studying the wood of art objects such as sculptures, panel paintings and furniture can be crucial to elucidate their chronology and production centre. Here we present an approach that considers the provenance of the wood and its potential availability in different areas as a means to identify the provenance of wooden art objects. We illustrate this approach with an interdisciplinary study aimed to determine the date and provenance of the Woman with lantern, a carved altar fragment from the Rijksmuseum's collections (Amsterdam, The Netherlands). The origin of this object is undocumented, but based on stylistic and iconographic features its provenance was proposed to be the altarpiece of Rennes cathedral (France), carved in Antwerp (Belgium) around 1520 C.E. However, doubts arose when curators tested the potential fit of the sculpture in that altarpiece and could not find a neat match. Dating and provenancing the wood of the sculpture by standard dendrochronological means failed to produce a date, and comparison of the tree-ring pattern from the sculpture with those of the sculptures from Rennes altarpiece delivered no results either, supporting the suspicion that the Woman with lantern belonged elsewhere. In 2019, X-ray computed tomography (CT) provided digital cross-sections throughout the sculpture and a longer tree-ring series was obtained. This time, the outermost ring was dated to the year 1487 C.E. The tree was estimated to have been cut after 1495 C.E., indicating a likely production in the first quarter of the 16th century. The origin of the timber in the eastern Netherlands/northwest Germany, combined with empirical evidence about timber availability in various regions of the Low Countries at that time, suggests that the sculpture was made in a workshop located north of the Rhine in the (current) Netherlands, rather than Antwerp. This research has led to the hypothesis that workshops north and south of the Rhine river branches in the Low Countries were supplied by forests located in different areas. If proven correct, establishing the wood provenance will assist in determining the origin of Netherlandish works of art from the late-Gothic and Northern Renaissance periods

Parallel-beam X-ray CT datasets of apples with internal defects and label balancing for machine learning

We present three parallel-beam tomographic datasets of 94 apples with internal defects along with defect label files. The datasets are prepared for development and testing of data-driven, learning-based image reconstruction, segmentation and post-processing methods. The three versions are a noiseless simulation; simulation with added Gaussian noise, and with scattering noise. The datasets are based on real 3D X-ray CT data and their subsequent volume reconstructions. The ground truth images, based on the volume reconstructions, are also available through this project. Apples contain various defects, which naturally introduce a label bias. We tackle this by formulating the bias as an optimization

Explorative imaging and its implementation at the FleX-ray laboratory

In tomographic imaging, the traditional process consists of an expert and an operator collecting data, the expert working on the reconstructed slices and drawing conclusions. The quality of reconstructions depends heavily on the quality of the collected data, except that, in the traditional process of imaging, the expert has very little influence over the acquisition parameters, experimental plan or the collected data. It is often the case that the expert has to draw limited conclusions from the reconstructions, or adapt a research question to data available. This method of imaging is static and sequential, and limits the potential of tomography as a research tool. In this paper, we propose a more dynamic process of imaging where experiments are tailored around a sample or the research question; intermediate reconstructions and analysis are available almost instantaneously, and expert has input at any stage of the process (including during acquisition) to improve acquisition or image reconstruction. Through various applications of 2D, 3D and dynamic 3D imaging at the FleX-ray Laboratory, we present the unexpected journey of exploration a research question undergoes, and the surprising benefits it yields

Looking under the skin: multi-scale CT scanning of a peculiarly constructed cornett in the Rijksmuseum

Covered tightly by a thin leather skin, three early seventeenth-century cornetts from the collection of the Rijksmuseum were examined with the focus on their construction and manufacturing. One cornett of the three unexpectedly turned out to have a peculiar construction and to be made out of two sections of different wood species. The question arose whether this could be original or is the result of an extensive restoration. As the internal structure is not accessible for analysis and examination, multi-scale Computed Tomography (CT) scanning was employed to identify the exact regions of interest (ROI) and subsequently perform scans at a sufficiently high resolution in those areas. 3D images of the hollow spaces such as the tunnelling structure caused by the common furniture beetle (Anobium punctatum) criss-crossing the wood species could be computed from the 3D x-ray tomography reconstruction. This allowed to place the occurrence of the insect infestation after the joining of the two sections. Fine tool-marks, signs of construction and potential indications of earlier treatments could be visualized. These results were compared with the other two instruments of the same group and cross-referenced to instruments in other collections, in an attempt to answer questions about the instrument's authenticity and originality. While the unusual construction out of two wood species might be the result of an extensive repair, another possible hypothesis-based on the combination of the results-is that this unique choice of original manufacturing was intentional, possibly to avoid splitting of the wood when inserting the mouthpiece or to counteract undesired vibrations when played.Algorithms and the Foundations of Software technolog