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

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

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

A tomographic workflow to enable deep learning for X-ray based foreign object detection

Detection of unwanted (‘foreign’) objects within products is a common procedure in many branches of industry for maintaining production quality. X-ray imaging is a fast, non-invasive and widely applicable method for foreign object detection. Deep learning has recently emerged as a powerful approach for recognizing patterns in radiographs (i.e., X-ray images), enabling automated X-ray based foreign object detection. However, these methods require a large number of training examples and manual annotation of these examples is a subjective and laborious task. In this work, we propose a Computed Tomography (CT) based method for producing training data for supervised learning of foreign object detection, with minimal labor requirements. In our approach, a few representative objects are CT scanned and reconstructed in 3D. The radiographs that are acquired as part of the CT-scan data serve as input for the machine learning method. High-quality ground truth locations of the foreign objects are obtained through accurate 3D reconstructions and segmentations. Using these segmented volumes, corresponding 2D segmentations are obtained by creating virtual projections. We outline the benefits of objectively and reproducibly generating training data in this way. In addition, we show how the accuracy depends on the number of objects used for the CT reconstructions. The results show that in this workflow generally only a relatively small number of representative objects (i.e., fewer than 10) are needed to achieve adequate detection performance in an industrial setting

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 c

Emulation of X-ray light-field cameras

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

Integrating expert feedback on the spot in a time-efficient explorative CT scanning workflow for cultural heritage objects

Computed Tomography (CT) has proven itself as a powerful technique for analysing the internal structure of cultural heritage objects. The process followed by conservators and technical art historians for investigating an object is explorative: each time a new question is asked based on the outcome of the previous investigation. This workflow however conflicts with the static nature of CT imaging, where the planning, execution and image analysis for a single CT scan can take days, or even weeks. A new question often requires conducting a new experiment, repeating the process of planning, execution and image analysis. This means that the time that is needed to complete the investigation is often longer than originally anticipated. In addition, it brings up more practical challenges such as the transportation of the object, facility availability and dependence on the imaging operator, as well as the cost of running additional experiments. A much needed interactive imaging process, where the user can adapt the CT scanning process based on the insights discovered on the spot, is hard to accomplish. Therefore, in this paper we show how a time-efficient explorative workflow can be created for CT investigation of art objects, where the object can be inspected in 3D while still in the scanner, and based on the observations and the resulting new questions, the scanning procedure can be iteratively refined. We identify the technical requirements for a CT scanner that can address the diversity in cultural heritage objects (size, shape, material composition), and the need for adaptive steering of the scanning process required for an explorative workflow. Our approach has been developed through the interdisciplinary research projects The See-Through Museum and Impact4Art. We demonstrate the key concepts by showing results of art objects scanned at the FleX-ray Laboratory at CWI, Amsterdam