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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., (© 2023. The Author(s).)

Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5,000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline., (© 2023. Springer Nature Limited.)

Late Pleistocene hominin postcranial specimens from Southeast Asia are relatively rare. Here we describe and place into temporal and geographic context two partial femora from the site of Trinil, Indonesia, which are dated stratigraphically and via Uranium-series direct dating to ca. 37-32 ka. The specimens, designated Trinil 9 and 10, include most of the diaphysis, with Trinil 9 being much better preserved. Microcomputed tomography is used to determine cross-sectional diaphyseal properties, with an emphasis on midshaft anteroposterior to mediolateral bending rigidity (I x /I y ), which has been shown to relate to both body shape and activity level in modern humans. The body mass of Trinil 9 is estimated from cortical area and reconstructed length using new equations based on a Pleistocene reference sample. Comparisons are carried out with a large sample of Pleistocene and Holocene East Asian, African, and European/West Asian femora. Our results show that Trinil 9 has a high I x /I y ratio, most consistent with a relatively narrow-bodied male from a mobile hunting-gathering population. It has an estimated body mass of 55.4 kg and a stature of 156 cm, which are small relative to Late Pleistocene males worldwide, but larger than the penecontemporaneous Deep Skull femur from Niah Cave, Malaysia, which is very likely female. This suggests the presence of small-bodied active hunter-gatherers in Southeast Asia during the later Late Pleistocene. Trinil 9 also contrasts strongly in morphology with earlier partial femora from Trinil dating to the late Early-early Middle Pleistocene (Femora II-V), and to a lesser extent with the well-known complete Femur I, most likely dating to the terminal Middle-early Late Pleistocene. Temporal changes in morphology among femoral specimens from Trinil parallel those observed in Homo throughout the Old World during the Pleistocene and document these differences within a single site., Competing Interests: Declaration of competing interest The authors declare that they have no competing interests., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Dating the wood from historical art objects is a crucial step to ascertain their production time, and support or refute attribution to an artist or a workshop. Dendrochronology is commonly used for this purpose but requires access to the tree-ring pattern in the wood, which can be hindered by preparatory layers, polychromy, wax, or integrated frames. Here we implemented non-invasive dendrochronology based on X-ray computed tomography (CT) to examine a painting on panel attributed to Rubens' studio and its presumed dating around 1636 CE. The CT images achieved a resolution of 37.3 micron and revealed a double panelling, which was concealed by oak strips covering all four edges. The back (visible) board is made of deciduous oak (Quercus subg. Quercus), the most common type of wood used in 17th-century Netherlandish workshops, and was dated terminus post quem after 1557 CE. However, the front (original) board used for the painting has been identified through examination of the wood anatomy as a tropical wood, probably Swietenia sp., a species seldom used in Netherlandish paintings, and remains undated. Its very presence attests the global character of 17th-century trade, and demonstrates the use of exotic species in Flemish studios. The date of the oak board refutes previous results and suggests that this board was trimmed to meet the size of the tropical one, having been glued to it for conservation purposes or with deceiving intentions to pretend that the painting was made on an oak panel. These revelations have opened new lines of art historical inquiry and highlight the potential of X-ray CT as a powerful tool for non-invasive study of historical art objects to retrieve their full history., Competing Interests: The authors have read the journal’s policy and have the following competing interests: The FleX-ray Laboratory at the CWI was established in collaboration between CWI, TESCAN-XRE NV, Nikhef, and Amsterdam Scientific Instruments (ASI). There are no patents, products in development or marketed products associated with this research to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Dendrochronology is an essential tool to determine the date and provenance of wood from historical art objects. As standard methods to access the tree rings are invasive, X-ray computed tomography (CT) has been proposed for non-invasive dendrochronological investigation. While traditional CT can provide clear images of the inner structure of wooden objects, it requires their full rotation, imposing strong limitations on the size of the object. These limitations have previously encouraged investigations into alternative acquisition trajectories, including trajectories with only linear movement. In this paper, we use such a line-trajectory (LT) X-ray tomography technique to retrieve tree-ring patterns from large wooden objects. We demonstrate that by moving a wooden artifact sideways between the static X-ray source and the detector during acquisition, sharp reconstruction images of the tree rings can be produced. We validate this technique using computer simulations and two wooden test planks, and demonstrate it on a large iconic chest from the Rijksmuseum collection (Amsterdam, The Netherlands). The LT scanning method can be easily implemented in standard X-ray imaging units available at museum research facilities. Therefore, this scanning technique represents a major step towards the standard implementation of non-invasive dendrochronology on large wooden cultural heritage objects.

The reconstruction of computed tomography (CT) images is an active area of research. Following the rise of deep learning methods, many data-driven models have been proposed in recent years. In this work, we present the results of a data challenge that we organized, bringing together algorithm experts from different institutes to jointly work on quantitative evaluation of several data-driven methods on two large, public datasets during a ten day sprint. We focus on two applications of CT, namely, low-dose CT and sparse-angle CT. This enables us to fairly compare different methods using standardized settings. As a general result, we observe that the deep learning-based methods are able to improve the reconstruction quality metrics in both CT applications while the top performing methods show only minor differences in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). We further discuss a number of other important criteria that should be taken into account when selecting a method, such as the availability of training data, the knowledge of the physical measurement model and the reconstruction speed.

X-ray plenoptic cameras acquire multi-view X-ray transmission images in a single exposure (light-field). Their development is challenging: designs have appeared only recently, and they are still affected by important limitations. Concurrently, the lack of available real X-ray light-field data hinders dedicated algorithmic development. Here, we present a physical emulation setup for rapidly exploring the parameter space of both existing and conceptual camera designs. This will assist and accelerate the design of X-ray plenoptic imaging solutions, and provide a tool for generating unlimited real X-ray plenoptic data. We also demonstrate that X-ray light-fields allow for reconstructing sharp spatial structures in three-dimensions (3D) from single-shot data.

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.

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.

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.