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The morphology in mixed bulk-heterojunction films are compared using three different quantitative measurement techniques. We compare the vertical composition changes using high-angle annular dark-field scanning transmission electron microscopy with electron tomography and neutron and x-ray reflectometry. The three measurement techniques yield qualitatively comparable vertical concentration measurements. The presence of a metal cathode during thermal annealing is observed to alter the fullerene concentration throughout the thickness of the film for all measurements. However, the absolute vertical concentration of fullerene is quantitatively different for the three measurements. The origin of the quantitative measurement differences is discussed. © 2014 Wiley Periodicals, Inc.

Deep-learning algorithms for cell segmentation typically require large data sets with high-quality annotations to be trained with. However, the annotation cost for obtaining such sets may prove to be prohibitively expensive. Our work aims to reduce the time necessary to create high-quality annotations of cell images by using a relatively small well-annotated data set for training a convolutional neural network to upgrade lower-quality annotations, produced at lower annotation costs. We investigate the performance of our solution when upgrading the annotation quality for labels affected by three types of annotation error: omission, inclusion, and bias. We observe that our method can upgrade annotations affected by high error levels from 0.3 to 0.9 Dice similarity with the ground-truth annotations. We also show that a relatively small well-annotated set enlarged with samples with upgraded annotations can be used to train better-performing cell segmentation networks compared to training only on the well-annotated set. Moreover, we present a use case where our solution can be successfully employed to increase the quality of the predictions of a segmentation network trained on just 10 annotated samples.

Visualizing the internal structure of museum objects is a crucial step in acquiring knowledge about the origin, state, and composition of cultural heritage artifacts. Among the most powerful techniques for exposing the interior of museum objects is computed tomography (CT), a technique that computationally forms a 3D image using hundreds of radiographs acquired in a full circular range. However, the lack of affordable and versatile CT equipment in museums, combined with the challenge of transporting precious collection objects, currently keeps this technique out of reach for most cultural heritage applications. We propose an approach for creating accurate CT reconstructions using only standard 2D radiography equipment already available in most larger museums. Specifically, we demonstrate that a combination of basic X-ray imaging equipment, a tailored marker-based image acquisition protocol, and sophisticated data-processing algorithms, can achieve 3D imaging of collection objects without the need for a costly CT imaging system. We implemented this approach in the British Museum (London), the J. Paul Getty Museum (Los Angeles), and the Rijksmuseum (Amsterdam). Our work paves the way for broad facilitation and adoption of CT technology across museums worldwide., (© 2024. The Author(s).)

Many of the recent successes of deep learning-based approaches have been enabled by a framework of flexible, composable computational blocks with their parameters adjusted through an automatic differentiation mechanism to implement various data processing tasks. In this work, we explore how the same philosophy can be applied to existing "classical" (i.e., non-learning) algorithms, focusing on computed tomography (CT) as application field. We apply four key design principles of this approach for CT workflow design: end-to-end optimization, explicit quality criteria, declarative algorithm construction by building the forward model, and use of existing classical algorithms as computational blocks. Through four case studies, we demonstrate that auto-differentiation is remarkably effective beyond the boundaries of neural-network training, extending to CT workflows containing varied combinations of classical and machine learning algorithms.

Premise: Most studies of the movement of orchid fruits and roots during plant development have focused on morphological observations; however, further genetic analysis is required to understand the molecular mechanisms underlying this phenomenon. A precise tool is required to observe these movements and harvest tissue at the correct position and time for transcriptomics research., Methods: We utilized three-dimensional (3D) micro-computed tomography (CT) scans to capture the movement of fast-growing Erycina pusilla roots, and built an integrated bioinformatics pipeline to process 3D images into 3D time-lapse videos. To record the movement of slowly developing E. pusilla and Phalaenopsis equestris fruits, two-dimensional (2D) photographs were used., Results: The E. pusilla roots twisted and resupinated multiple times from early development. The first period occurred in the early developmental stage (77-84 days after germination [DAG]) and the subsequent period occurred later in development (140-154 DAG). While E. pusilla fruits twisted 45° from 56-63 days after pollination (DAP), the fruits of P. equestris only began to resupinate a week before dehiscence (133 DAP) and ended a week after dehiscence (161 DAP)., Discussion: Our methods revealed that each orchid root and fruit had an independent direction and degree of torsion from the initial to the final position. Our innovative approaches produced detailed spatial and temporal information on the resupination of roots and fruits during orchid development., (© 2024 The Authors. Applications in Plant Sciences published by Wiley Periodicals LLC on behalf of Botanical Society of America.)

Background: X-ray imaging is widely used for the non-destructive detection of defects in industrial products on a conveyor belt. In-line detection requires highly accurate, robust, and fast algorithms. Deep Convolutional Neural Networks (DCNNs) satisfy these requirements when a large amount of labeled data is available. To overcome the challenge of collecting these data, different methods of X-ray image generation are considered., Objective: Depending on the desired degree of similarity to real data, different physical effects should either be simulated or can be ignored. X-ray scattering is known to be computationally expensive to simulate, and this effect can greatly affect the accuracy of a generated X-ray image. We aim to quantitatively evaluate the effect of scattering on defect detection., Methods: Monte-Carlo simulation is used to generate X-ray scattering distribution. DCNNs are trained on the data with and without scattering and applied to the same test datasets. Probability of Detection (POD) curves are computed to compare their performance, characterized by the size of the smallest detectable defect., Results: We apply the methodology to a model problem of defect detection in cylinders. When trained on data without scattering, DCNNs reliably detect defects larger than 1.3 mm, and using data with scattering improves performance by less than 5%. If the analysis is performed on the cases with large scattering-to-primary ratio (1 < SPR < 5), the difference in performance could reach 15% (approx. 0.4 mm)., Conclusion: Excluding the scattering signal from the training data has the largest effect on the smallest detectable defects, and the difference decreases for larger defects. The scattering-to-primary ratio has a significant effect on detection performance and the required accuracy of data generation.

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).)

In the fine arts, impressions found on terracotta sculptures in museum collections are scarcely reported and not in a systematic manner. Here, we present a procedure for scanning fingermarks and toolmarks found on the visible surface and inner walls of a terracotta sculpture using 3D micro-computed tomography, as well as methods for quantitatively characterizing these impressions. We apply our pipeline on the terracotta sculpture Study for a Hovering Putto , attributed to Laurent Delvaux and housed in the Rijksmuseum collection. On the basis of combined archaeology and forensics research that assigns age groups to makers of European ancestry from ridge breadth values, we estimate that the fingermarks belong to an adult male. Given that each fingerprint is unique and the carving tools were exclusively made in the artist's workshop, we give incentive to aim for artist profiling using innovative computational approaches on preserved impressions from terracotta sculptures.

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.)

Electron tomography is a widely used technique for 3D structural analysis of nanomaterials, but it can cause damage to samples due to high electron doses and long exposure times. To minimize such damage, researchers often reduce beam exposure by acquiring fewer projections through tilt undersampling. However, this approach can also introduce reconstruction artifacts due to insufficient sampling. Therefore, it is important to determine the optimal number of projections that minimizes both beam exposure and undersampling artifacts for accurate reconstructions of beam-sensitive samples. Current methods for determining this optimal number of projections involve acquiring and post-processing multiple reconstructions with different numbers of projections, which can be time-consuming and requires multiple samples due to sample damage. To improve this process, we propose a protocol that combines golden ratio scanning and quasi-3D reconstruction to estimate the optimal number of projections in real-time during a single acquisition. This protocol was validated using simulated and realistic nanoparticles, and was successfully applied to reconstruct two beam-sensitive metal-organic framework complexes.

Many advanced nanomaterials rely on carefully designed morphology and elemental distribution to achieve their functionalities. Among the few experimental techniques that can directly visualise the 3D elemental distribution on the nanoscale are approaches based on electron tomography in combination with energy-dispersive X-ray spectroscopy (EDXS) and electron energy loss spectroscopy (EELS). Unfortunately, these highly informative methods are severely limited by the fundamentally low signal-to-noise ratio, which makes long experimental times and high electron irradiation doses necessary to obtain reliable 3D reconstructions. Addressing these limitations has been the major research question for the development of these techniques in recent years. This short review outlines the latest progress on the methods to reduce experimental time and electron irradiation dose requirements for 3D elemental distribution analysis and gives an outlook on the development of this field in the near future., (© 2022 Royal Microscopical Society.)

Although X-ray imaging is used routinely in industry for high-throughput product quality control, its capability to detect internal defects has strong limitations. The main challenge stems from the superposition of multiple object features within a single X-ray view. Deep Convolutional neural networks can be trained by annotated datasets of X-ray images to detect foreign objects in real-time. However, this approach depends heavily on the availability of a large amount of data, strongly hampering the viability of industrial use with high variability between batches of products. We present a computationally efficient, CT-based approach for creating artificial single-view X-ray data based on just a few physically CT-scanned objects. By algorithmically modifying the CT-volume, a large variety of training examples is obtained. Our results show that applying the generative model to a single CT-scanned object results in image analysis accuracy that would otherwise be achieved with scans of tens of real-world samples. Our methodology leads to a strong reduction in training data needed, improved coverage of the combinations of base and foreign objects, and extensive generalizability to additional features. Once trained on just a single CT-scanned object, the resulting deep neural network can detect foreign objects in real-time with high accuracy., (© 2023. The Author(s).)

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.)

Computer-assisted surgery (CAS) allows clinicians to personalize treatments and surgical interventions and has therefore become an increasingly popular treatment modality in maxillofacial surgery. The current maxillofacial CAS consists of three main steps: (1) CT image reconstruction, (2) bone segmentation, and (3) surgical planning. However, each of these three steps can introduce errors that can heavily affect the treatment outcome. As a consequence, tedious and time-consuming manual post-processing is often necessary to ensure that each step is performed adequately. One way to overcome this issue is by developing and implementing neural networks (NNs) within the maxillofacial CAS workflow. These learning algorithms can be trained to perform specific tasks without the need for explicitly defined rules. In recent years, an extremely large number of novel NN approaches have been proposed for a wide variety of applications, which makes it a difficult task to keep up with all relevant developments. This study therefore aimed to summarize and review all relevant NN approaches applied for CT image reconstruction, bone segmentation, and surgical planning. After full text screening, 76 publications were identified: 32 focusing on CT image reconstruction, 33 focusing on bone segmentation and 11 focusing on surgical planning. Generally, convolutional NNs were most widely used in the identified studies, although the multilayer perceptron was most commonly applied in surgical planning tasks. Moreover, the drawbacks of current approaches and promising research avenues are discussed.

Tomographic algorithms are often compared by evaluating them on certain benchmark datasets. For fair comparison, these datasets should ideally (i) be challenging to reconstruct, (ii) be representative of typical tomographic experiments, (iii) be flexible to allow for different acquisition modes, and (iv) include enough samples to allow for comparison of data-driven algorithms. Current approaches often satisfy only some of these requirements, but not all. For example, real-world datasets are typically challenging and representative of a category of experimental examples, but are restricted to the acquisition mode that was used in the experiment and are often limited in the number of samples. Mathematical phantoms are often flexible and can sometimes produce enough samples for data-driven approaches, but can be relatively easy to reconstruct and are often not representative of typical scanned objects. In this paper, we present a family of foam-like mathematical phantoms that aims to satisfy all four requirements simultaneously. The phantoms consist of foam-like structures with more than 100000 features, making them challenging to reconstruct and representative of common tomography samples. Because the phantoms are computer-generated, varying acquisition modes and experimental conditions can be simulated. An effectively unlimited number of random variations of the phantoms can be generated, making them suitable for data-driven approaches. We give a formal mathematical definition of the foam-like phantoms, and explain how they can be generated and used in virtual tomographic experiments in a computationally efficient way. In addition, several 4D extensions of the 3D phantoms are given, enabling comparisons of algorithms for dynamic tomography. Finally, example phantoms and tomographic datasets are given, showing that the phantoms can be effectively used to make fair and informative comparisons between tomography algorithms., (open access.)

For reconstructing large tomographic datasets fast, filtered backprojection-type or Fourier-based algorithms are still the method of choice, as they have been for decades. These robust and computationally efficient algorithms have been integrated in a broad range of software packages. The continuous mathematical formulas used for image reconstruction in such algorithms are unambiguous. However, variations in discretization and interpolation result in quantitative differences between reconstructed images, and corresponding segmentations, obtained from different software. This hinders reproducibility of experimental results, making it difficult to ensure that results and conclusions from experiments can be reproduced at different facilities or using different software. In this paper, a way to reduce such differences by optimizing the filter used in analytical algorithms is proposed. These filters can be computed using a wrapper routine around a black-box implementation of a reconstruction algorithm, and lead to quantitatively similar reconstructions. Use cases for this approach are demonstrated by computing implementation-adapted filters for several open-source implementations and applying them to simulated phantoms and real-world data acquired at the synchrotron. Our contribution to a reproducible reconstruction step forms a building block towards a fully reproducible synchrotron tomography data processing pipeline., (open access.)

Background and Objectives: Deep learning is being increasingly used for deformable image registration and unsupervised approaches, in particular, have shown great potential. However, the registration of abdominopelvic Computed Tomography (CT) images remains challenging due to the larger displacements compared to those in brain or prostate Magnetic Resonance Imaging datasets that are typically considered as benchmarks. In this study, we investigate the use of the commonly used unsupervised deep learning framework VoxelMorph for the registration of a longitudinal abdominopelvic CT dataset acquired in patients with bone metastases from breast cancer., Methods: As a pre-processing step, the abdominopelvic CT images were refined by automatically removing the CT table and all other extra-corporeal components. To improve the learning capabilities of the VoxelMorph framework when only a limited amount of training data is available, a novel incremental training strategy is proposed based on simulated deformations of consecutive CT images in the longitudinal dataset. This devised training strategy was compared against training on simulated deformations of a single CT volume. A widely used software toolbox for deformable image registration called NiftyReg was used as a benchmark. The evaluations were performed by calculating the Dice Similarity Coefficient (DSC) between manual vertebrae segmentations and the Structural Similarity Index (SSIM)., Results: The CT table removal procedure allowed both VoxelMorph and NiftyReg to achieve significantly better registration performance. In a 4-fold cross-validation scheme, the incremental training strategy resulted in better registration performance compared to training on a single volume, with a mean DSC of 0.929±0.037 and 0.883±0.033, and a mean SSIM of 0.984±0.009 and 0.969±0.007, respectively. Although our deformable image registration method did not outperform NiftyReg in terms of DSC (0.988±0.003) or SSIM (0.995±0.002), the registrations were approximately 300 times faster., Conclusions: This study showed the feasibility of deep learning based deformable registration of longitudinal abdominopelvic CT images via a novel incremental training strategy based on simulated deformations., Competing Interests: Declaration of Competing Interest All authors in this paper do not have any conflict of interest., (Copyright © 2021 The Author(s). Published by Elsevier B.V. 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.

Background and Objective: Over the past decade, convolutional neural networks (CNNs) have revolutionized the field of medical image segmentation. Prompted by the developments in computational resources and the availability of large datasets, a wide variety of different two-dimensional (2D) and three-dimensional (3D) CNN training strategies have been proposed. However, a systematic comparison of the impact of these strategies on the image segmentation performance is still lacking. Therefore, this study aimed to compare eight different CNN training strategies, namely 2D (axial, sagittal and coronal slices), 2.5D (3 and 5 adjacent slices), majority voting, randomly oriented 2D cross-sections and 3D patches., Methods: These eight strategies were used to train a U-Net and an MS-D network for the segmentation of simulated cone-beam computed tomography (CBCT) images comprising randomly-placed non-overlapping cylinders and experimental CBCT images of anthropomorphic phantom heads. The resulting segmentation performances were quantitatively compared by calculating Dice similarity coefficients. In addition, all segmented and gold standard experimental CBCT images were converted into virtual 3D models and compared using orientation-based surface comparisons., Results: The CNN training strategy that generally resulted in the best performances on both simulated and experimental CBCT images was majority voting. When employing 2D training strategies, the segmentation performance can be optimized by training on image slices that are perpendicular to the predominant orientation of the anatomical structure of interest. Such spatial features should be taken into account when choosing or developing novel CNN training strategies for medical image segmentation., Conclusions: The results of this study will help clinicians and engineers to choose the most-suited CNN training strategy for CBCT image segmentation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021. Published by Elsevier B.V.)

Accurate segmentation of the jaw (i.e., mandible and maxilla) and the teeth in cone beam computed tomography (CBCT) scans is essential for orthodontic diagnosis and treatment planning. Although various (semi)automated methods have been proposed to segment the jaw or the teeth, there is still a lack of fully automated segmentation methods that can simultaneously segment both anatomic structures in CBCT scans (i.e., multiclass segmentation). In this study, we aimed to train and validate a mixed-scale dense (MS-D) convolutional neural network for multiclass segmentation of the jaw, the teeth, and the background in CBCT scans. Thirty CBCT scans were obtained from patients who had undergone orthodontic treatment. Gold standard segmentation labels were manually created by 4 dentists. As a benchmark, we also evaluated MS-D networks that segmented the jaw or the teeth (i.e., binary segmentation). All segmented CBCT scans were converted to virtual 3-dimensional (3D) models. The segmentation performance of all trained MS-D networks was assessed by the Dice similarity coefficient and surface deviation. The CBCT scans segmented by the MS-D network demonstrated a large overlap with the gold standard segmentations (Dice similarity coefficient: 0.934 ± 0.019, jaw; 0.945 ± 0.021, teeth). The MS-D network-based 3D models of the jaw and the teeth showed minor surface deviations when compared with the corresponding gold standard 3D models (0.390 ± 0.093 mm, jaw; 0.204 ± 0.061 mm, teeth). The MS-D network took approximately 25 s to segment 1 CBCT scan, whereas manual segmentation took about 5 h. This study showed that multiclass segmentation of jaw and teeth was accurate and its performance was comparable to binary segmentation. The MS-D network trained for multiclass segmentation would therefore make patient-specific orthodontic treatment more feasible by strongly reducing the time required to segment multiple anatomic structures in CBCT scans.

High cone-angle artifacts (HCAAs) appear frequently in circular cone-beam computed tomography (CBCT) images and can heavily affect diagnosis and treatment planning. To reduce HCAAs in CBCT scans, we propose a novel deep learning approach that reduces the three-dimensional (3D) nature of HCAAs to two-dimensional (2D) problems in an efficient way. Specifically, we exploit the relationship between HCAAs and the rotational scanning geometry by training a convolutional neural network (CNN) using image slices that were radially sampled from CBCT scans. We evaluated this novel approach using a dataset of input CBCT scans affected by HCAAs and high-quality artifact-free target CBCT scans. Two different CNN architectures were employed, namely U-Net and a mixed-scale dense CNN (MS-D Net). The artifact reduction performance of the proposed approach was compared to that of a Cartesian slice-based artifact reduction deep learning approach in which a CNN was trained to remove the HCAAs from Cartesian slices. In addition, all processed CBCT scans were segmented to investigate the impact of HCAAs reduction on the quality of CBCT image segmentation. We demonstrate that the proposed deep learning approach with geometry-aware dimension reduction greatly reduces HCAAs in CBCT scans and outperforms the Cartesian slice-based deep learning approach. Moreover, the proposed artifact reduction approach markedly improves the accuracy of the subsequent segmentation task compared to the Cartesian slice-based workflow., (© 2021 Institute of Physics and Engineering in Medicine.)

X-ray imaging is a widely used technique for non-destructive inspection of agricultural food products. One application of X-ray imaging is the autonomous, in-line detection of foreign objects in food samples. Examples of such inclusions are bone fragments in meat products, plastic and metal debris in fish, and fruit infestations. This article presents a processing methodology for unsupervised foreign object detection based on dual-energy X-ray absorptiometry (DEXA). A novel thickness correction model is introduced as a pre-processing technique for DEXA data. The aim of the model is to homogenize regions in the image that belong to the food product and to enhance contrast where the foreign object is present. In this way, the segmentation of the foreign object is more robust to noise and lack of contrast. The proposed methodology was applied to a dataset of 488 samples of meat products acquired from a conveyor belt. Approximately 60% of the samples contain foreign objects of different types and sizes, while the rest of the samples are void of foreign objects. The results show that samples without foreign objects are correctly identified in 97% of cases and that the overall accuracy of foreign object detection reaches 95%.

Synchrotron X-ray tomography enables the examination of the internal structure of materials at submicron spatial resolution and subsecond temporal resolution. Unavoidable experimental constraints can impose dose and time limits on the measurements, introducing noise in the reconstructed images. Convolutional neural networks (CNNs) have emerged as a powerful tool to remove noise from reconstructed images. However, their training typically requires collecting a dataset of paired noisy and high-quality measurements, which is a major obstacle to their use in practice. To circumvent this problem, methods for CNN-based denoising have recently been proposed that require no separate training data beyond the already available noisy reconstructions. Among these, the Noise2Inverse method is specifically designed for tomography and related inverse problems. To date, applications of Noise2Inverse have only taken into account 2D spatial information. In this paper, we expand the application of Noise2Inverse in space, time, and spectrum-like domains. This development enhances applications to static and dynamic micro-tomography as well as X-ray diffraction tomography. Results on real-world datasets establish that Noise2Inverse is capable of accurate denoising and enables a substantial reduction in acquisition time while maintaining image quality.

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.

Circular cone-beam (CCB) Computed Tomography (CT) has become an integral part of industrial quality control, materials science and medical imaging. The need to acquire and process each scan in a short time naturally leads to trade-offs between speed and reconstruction quality, creating a need for fast reconstruction algorithms capable of creating accurate reconstructions from limited data. In this paper, we introduce the Neural Network Feldkamp-Davis-Kress (NN-FDK) algorithm. This algorithm adds a machine learning component to the FDK algorithm to improve its reconstruction accuracy while maintaining its computational efficiency. Moreover, the NN-FDK algorithm is designed such that it has low training data requirements and is fast to train. This ensures that the proposed algorithm can be used to improve image quality in high-throughput CT scanning settings, where FDK is currently used to keep pace with the acquisition speed using readily available computational resources. We compare the NN-FDK algorithm to two standard CT reconstruction algorithms and to two popular deep neural networks trained to remove reconstruction artifacts from the 2D slices of an FDK reconstruction. We show that the NN-FDK reconstruction algorithm is substantially faster in computing a reconstruction than all the tested alternative methods except for the standard FDK algorithm and we show it can compute accurate CCB CT reconstructions in cases of high noise, a low number of projection angles or large cone angles. Moreover, we show that the training time of an NN-FDK network is orders of magnitude lower than the considered deep neural networks, with only a slight reduction in reconstruction accuracy.

An important challenge in hyperspectral imaging tasks is to cope with the large number of spectral bins. Common spectral data reduction methods do not take prior knowledge about the task into account. Consequently, sparsely occurring features that may be essential for the imaging task may not be preserved in the data reduction step. Convolutional neural network (CNN) approaches are capable of learning the specific features relevant to the particular imaging task, but applying them directly to the spectral input data is constrained by the computational efficiency. We propose a novel supervised deep learning approach for combining data reduction and image analysis in an end-to-end architecture. In our approach, the neural network component that performs the reduction is trained such that image features most relevant for the task are preserved in the reduction step. Results for two convolutional neural network architectures and two types of generated datasets show that the proposed Data Reduction CNN (DRCNN) approach can produce more accurate results than existing popular data reduction methods, and can be used in a wide range of problem settings. The integration of knowledge about the task allows for more image compression and higher accuracies compared to standard data reduction methods.

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.

Tomographic X-ray microscopy beamlines at synchrotron light sources worldwide have pushed the achievable time-resolution for dynamic 3-dimensional structural investigations down to a fraction of a second, allowing the study of quickly evolving systems. The large data rates involved impose heavy demands on computational resources, making it difficult to readily process and interrogate the resulting volumes. The data acquisition is thus performed essentially blindly. Such a sequential process makes it hard to notice problems with the measurement protocol or sample conditions, potentially rendering the acquired data unusable, and it keeps the user from optimizing the experimental parameters of the imaging task at hand. We present an efficient approach to address this issue based on the real-time reconstruction, visualisation and on-the-fly analysis of a small number of arbitrarily oriented slices. This solution, requiring only a single additional computing workstation, has been implemented at the TOMCAT beamline of the Swiss Light Source. The system is able to process multiple sets of slices per second, thus pushing the reconstruction throughput on the same level as the data acquisition. This enables the monitoring of dynamic processes as they occur and represents the next crucial step towards adaptive feedback control of time-resolved in situ tomographic experiments.

Purpose: In order to attain anatomical models, surgical guides and implants for computer-assisted surgery, accurate segmentation of bony structures in cone-beam computed tomography (CBCT) scans is required. However, this image segmentation step is often impeded by metal artifacts. Therefore, this study aimed to develop a mixed-scale dense convolutional neural network (MS-D network) for bone segmentation in CBCT scans affected by metal artifacts., Method: Training data were acquired from 20 dental CBCT scans affected by metal artifacts. An experienced medical engineer segmented the bony structures in all CBCT scans using global thresholding and manually removed all remaining noise and metal artifacts. The resulting gold standard segmentations were used to train an MS-D network comprising 100 convolutional layers using far fewer trainable parameters than alternative convolutional neural network (CNN) architectures. The bone segmentation performance of the MS-D network was evaluated using a leave-2-out scheme and compared with a clinical snake evolution algorithm and two state-of-the-art CNN architectures (U-Net and ResNet). All segmented CBCT scans were subsequently converted into standard tessellation language (STL) models and geometrically compared with the gold standard., Results: CBCT scans segmented using the MS-D network, U-Net, ResNet and the snake evolution algorithm demonstrated mean Dice similarity coefficients of 0.87 ± 0.06, 0.87 ± 0.07, 0.86 ± 0.05, and 0.78 ± 0.07, respectively. The STL models acquired using the MS-D network, U-Net, ResNet and the snake evolution algorithm demonstrated mean absolute deviations of 0.44 mm ± 0.13 mm, 0.43 mm ± 0.16 mm, 0.40 mm ± 0.12 mm and 0.57 mm ± 0.22 mm, respectively. In contrast to the MS-D network, the ResNet introduced wave-like artifacts in the STL models, whereas the U-Net incorrectly labeled background voxels as bone around the vertebrae in 4 of the 9 CBCT scans containing vertebrae., Conclusion: The MS-D network was able to accurately segment bony structures in CBCT scans affected by metal artifacts., (© 2019 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

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.

Recently we have shown that light-field photography images can be interpreted as limited-angle cone-beam tomography acquisitions. Here, we use this property to develop a direct-space tomographic refocusing formulation that allows one to refocus both unfocused and focused light-field images. We express the reconstruction as a convex optimization problem, thus enabling the use of various regularization terms to help suppress artifacts, and a wide class of existing advanced tomographic algorithms. This formulation also supports super-resolved reconstructions and the correction of the optical system's limited frequency response (point spread function). We validate this method with numerical and real-world examples.

Energy-dispersive X-ray spectroscopic (EDS) tomography is a powerful three-dimensional (3D) imaging technique for characterizing the chemical composition and structure of nanomaterials. However, the accuracy and resolution are typically hampered by the limited number of tilt images that can be measured and the low signal-to-noise ratios (SNRs) of the energy-resolved tilt images. Various sophisticated reconstruction algorithms have been proposed for specific types of samples and imaging conditions, yet deciding on which algorithm to use for each new case remains a complex problem. In this paper, we propose to tailor the reconstruction algorithm for EDS tomography in three aspects: (1) model the reconstruction problem based on an accurate assumption of the data statistics; (2) regularize the reconstruction to incorporate prior knowledge; (3) apply bimodal tomography to augment the EDS data with a high-SNR modality. Methods for the three aspects can be combined in one reconstruction procedure as three modules. Therefore, a reconstruction algorithm can be constructed as a 'recipe'. We also provide guidelines for preparing the recipe based on conditions and assumptions for the data. We investigate the effects of different recipes on both simulated data and real experimental data. The results show that the preferred recipe depends on both acquisition conditions and sample properties, and that the image quality can be enhanced using a properly tailored recipe., (Copyright © 2018. Published by Elsevier B.V.)

We propose a combination of an experimental approach and a reconstruction technique that leads to reduction of artefacts in X-ray computer tomography of strongly attenuating objects. Through fully automatic data alignment, data generated in multiple experiments with varying object orientations are combined. Simulations and experiments show that the solutions computed using algebraic methods based on multiple acquisitions can achieve a dramatic improvement in the reconstruction quality, even when each acquisition generates a reduced number of projections. The approach does not require any advanced setup components making it ideal for laboratory-based X-ray tomography.

Current computational methods for light field photography model the ray-tracing geometry inside the plenoptic camera. This representation of the problem, and some common approximations, can lead to errors in the estimation of object sizes and positions. We propose a representation that leads to the correct reconstruction of object sizes and distances to the camera, by showing that light field images can be interpreted as limited angle cone-beam tomography acquisitions. We then quantitatively analyze its impact on image refocusing, depth estimation and volumetric reconstructions, comparing it against other possible representations. Finally, we validate these results with numerical and real-world examples.

Energy-dispersive X-ray spectroscopy (EDS) tomography is an advanced technique to characterize compositional information for nanostructures in three dimensions (3D). However, the application is hindered by the poor image quality caused by the low signal-to-noise ratios and the limited number of tilts, which are fundamentally limited by the insufficient number of X-ray counts. In this paper, we explore how to make accurate EDS reconstructions from such data. We propose to augment EDS tomography by joining with it a more accurate high-angle annular dark-field STEM (HAADF-STEM) tomographic reconstruction, for which usually a larger number of tilt images are feasible. This augmentation is realized through total nuclear variation (TNV) regularization, which encourages the joint EDS and HAADF reconstructions to have not only sparse gradients but also common edges and parallel (or antiparallel) gradients. Our experiments show that reconstruction images are more accurate compared to the non-regularized and the total variation regularized reconstructions, even when the number of tilts is small or the X-ray counts are low., (Copyright © 2018 Elsevier B.V. All rights reserved.)

HAADF-STEM tomography is a common technique for characterizing the three-dimensional morphology of nanomaterials. In conventional tomographic reconstruction algorithms, the image intensity is assumed to be a linear projection of a physical property of the specimen. However, this assumption of linearity is not completely valid due to the nonlinear damping of signal intensities. The nonlinear damping effects increase w.r.t the specimen thickness and lead to so-called "cupping artifacts", due to a mismatch with the linear model used in the reconstruction algorithm. Moreover, nonlinear damping effects can strongly limit the applicability of advanced reconstruction approaches such as Total Variation Minimization and discrete tomography. In this paper, we propose an algorithm for automatically correcting the nonlinear effects and the subsequent cupping artifacts. It is applicable to samples in which chemical compositions can be segmented based on image gray levels. The correction is realized by iteratively estimating the nonlinear relationship between projection intensity and sample thickness, based on which the projections are linearized. The correction and reconstruction algorithms are tested on simulated and experimental data., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Self-assembling structures and their dynamical processes in polymeric systems have been investigated using three-dimensional transmission electron microscopy (3D-TEM). Block copolymers (BCPs) self-assemble into nanoscale periodic structures called microphase-separated structures, a deep understanding of which is important for creating nanomaterials with superior physical properties, such as high-performance membranes with well-defined pore size and high-density data storage media. Because microphase-separated structures have become increasingly complicated with advances in precision polymerization, characterizing these complex morphologies is becoming increasingly difficult. Thus, microscopes capable of obtaining 3D images are required. In this article, we demonstrate that 3D-TEM is an essential tool for studying BCP nanostructures, especially those self-assembled during dynamical processes and under confined conditions. The first example is a dynamical process called order-order transitions (OOTs). Upon changing temperature or pressure or applying an external field, such as a shear flow or electric field, BCP nanostructures transform from one type of structure to another. The OOTs are examined by freezing the specimens in the middle of the OOT and then observing the boundary structures between the preexisting and newly formed nanostructures in three-dimensions. In an OOT between the bicontinuous double gyroid and hexagonally packed cylindrical structures, two different types of epitaxial phase transition paths are found. Interestingly, the paths depend on the direction of the OOT. The second example is BCP self-assemblies under confinement that have been examined by 3D-TEM. A variety of intriguing and very complicated 3D morphologies can be formed even from the BCPs that self-assemble into simple nanostructures, such as lamellar and cylindrical structures in the bulk (in free space). Although 3D-TEM is becoming more frequently used for detailed morphological investigations, it is generally used to study static nanostructures. Although OOTs are dynamical processes, the actual experiment is done in the static state, through a detailed morphological study of a snapshot taken during the OOT. Developing time-dependent nanoscale 3D imaging has become a hot topic. Here, the two main problems preventing the development of in situ electron tomography for polymer materials are addressed. First, the staining protocol often used to enhance contrast for electrons is replaced by a new contrast enhancement based on chemical differences between polymers. In this case, no staining is necessary. Second, a new 3D reconstruction algorithm allows us to obtain a high-contrast, quantitative 3D image from fewer projections than is required for the conventional algorithm to achieve similar contrast, reducing the number of projections and thus the electron beam dose. Combining these two new developments is expected to open new doors to 3D in situ real-time structural observation of polymer materials.

Tomographic reconstruction algorithms offer a means by which a tilt-series of transmission images can be combined to yield a three dimensional model of the specimen. Conventional reconstruction algorithms assume that the measured signal is a linear projection of some property, typically the density, of the material. Here we report the use of multislice simulations to investigate the extent to which this assumption is met in HAADF-STEM imaging. The use of simulations allows for a systematic survey of a range of materials and microscope parameters to inform optimal experimental design. Using this approach it is demonstrated that the imaging of amorphous materials is in good agreement with the projection assumption in most cases. Images of crystalline specimens taken along zone-axes are found to be poorly suited for conventional linear reconstruction algorithms due to channelling effects which produce enhanced intensities compared with off-axis images, and poor compliance with the projection requirement. Off-axis images are found to be suitable for reconstruction, though they do not strictly meet the linearity requirement in most cases. It is demonstrated that microscope parameters can be selected to yield improved compliance with the projection requirement., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Electron tomography is an essential imaging technique for the investigation of morphology and 3D structure of nanomaterials. This method, however, suffers from well-known missing wedge artifacts due to a restricted tilt range, which limits the objectiveness, repeatability and efficiency of quantitative structural analysis. Discrete tomography represents one of the promising reconstruction techniques for materials science, potentially capable of delivering higher fidelity reconstructions by exploiting the prior knowledge of the limited number of material compositions in a specimen. However, the application of discrete tomography to practical datasets remains a difficult task due to the underlying challenging mathematical problem. In practice, it is often hard to obtain consistent reconstructions from experimental datasets. In addition, numerous parameters need to be tuned manually, which can lead to bias and non-repeatability. In this paper, we present the application of a new iterative reconstruction technique, named TVR-DART, for discrete electron tomography. The technique is capable of consistently delivering reconstructions with significantly reduced missing wedge artifacts for a variety of challenging data and imaging conditions, and can automatically estimate its key parameters. We describe the principles of the technique and apply it to datasets from three different types of samples acquired under diverse imaging modes. By further reducing the available tilt range and number of projections, we show that the proposed technique can still produce consistent reconstructions with minimized missing wedge artifacts. This new development promises to provide the electron microscopy community with an easy-to-use and robust tool for high-fidelity 3D characterization of nanomaterials., (Copyright © 2017 Elsevier B.V. All rights reserved.)

A three-dimensional (3D) chemical characterization of nanomaterials can be obtained using tomography based on high angle annular dark field (HAADF) scanning transmission electron microscopy (STEM) or energy dispersive X-ray spectroscopy (EDS) STEM. These two complementary techniques have both advantages and disadvantages. The Z-contrast images have good image quality but lack robustness in the compositional analysis, while the elemental maps give more element-specific information, but at a low signal-to-noise ratio and a longer exposure time. Our aim is to combine these two types of complementary information in one single tomographic reconstruction process. Therefore, an imaging model is proposed combining both HAADF-STEM and EDS-STEM. Based on this model, the elemental distributions can be reconstructed using both types of information simultaneously during the reconstruction process. The performance of the new technique is evaluated using simulated data and real experimental data. The results demonstrate that combining two imaging modalities leads to tomographic reconstructions with suppressed noise and enhanced contrast., (Copyright © 2016 Elsevier B.V. All rights reserved.)

While iterative reconstruction algorithms for tomography have several advantages compared to standard backprojection methods, the adoption of such algorithms in large-scale imaging facilities is still limited, one of the key obstacles being their high computational load. Although GPU-enabled computing clusters are, in principle, powerful enough to carry out iterative reconstructions on large datasets in reasonable time, creating efficient distributed algorithms has so far remained a complex task, requiring low-level programming to deal with memory management and network communication. The ASTRA toolbox is a software toolbox that enables rapid development of GPU accelerated tomography algorithms. It contains GPU implementations of forward and backprojection operations for many scanning geometries, as well as a set of algorithms for iterative reconstruction. These algorithms are currently limited to using GPUs in a single workstation. In this paper, we present an extension of the ASTRA toolbox and its Python interface with implementations of forward projection, backprojection and the SIRT algorithm that can be distributed over multiple GPUs and multiple workstations, as well as the tools to write distributed versions of custom reconstruction algorithms, to make processing larger datasets with ASTRA feasible. As a result, algorithms that are implemented in a high-level conceptual script can run seamlessly on GPU-enabled computing clusters, up to 32 GPUs or more. Our approach is not limited to slice-based reconstruction, facilitating a direct portability of algorithms coded for parallel-beam synchrotron tomography to cone-beam laboratory tomography setups without making changes to the reconstruction algorithm.

Purpose: Cerebral perfusion x-ray computed tomography (PCT) is a powerful tool for noninvasive imaging of hemodynamic information throughout the brain. Conventional PCT requires the brain to be imaged multiple times during the perfusion process, and hence radiation dose is a major concern. The authors propose a PCT reconstruction algorithm that allows for lowering the dose while maintaining a high quality of the perfusion maps. It relies on an accurate estimation of the arterial input function (AIF), which in turn depends on the quality of the attenuation curves in the arterial region., Methods: The authors propose the local attenuation curve optimization (LACO) framework. It accurately models the attenuation curves inside the vessel and arterial regions and optimizes its shape directly based on the acquired x-ray projection data., Results: The LACO algorithm is extensively validated with simulation and real clinical experiments. Quantitative and qualitative results show that our proposed approach accurately estimates the vessel and arterial attenuation curves from only few x-ray projections. In contrast to conventional approaches, where the AIF is estimated based on the reconstructed images, our method computes an optimal AIF directly based on the projection data, resulting in far more accurate perfusion maps., Conclusions: The LACO algorithm allows estimating high quality perfusion maps in low dose scanning protocols.

Electron tomography is a powerful technique for the 3D characterization of the morphology of nanostructures. Nevertheless, resolving the chemical composition of complex nanostructures in 3D remains challenging and the number of studies in which electron energy loss spectroscopy (EELS) is combined with tomography is limited. During the last decade, dedicated reconstruction algorithms have been developed for HAADF-STEM tomography using prior knowledge about the investigated sample. Here, we will use the prior knowledge that the experimental spectrum of each reconstructed voxel is a linear combination of a well-known set of references spectra in a so-called direct spectroscopic tomography technique. Based on a simulation experiment, it is shown that this technique provides superior results in comparison to conventional reconstruction methods for spectroscopic data, especially for spectrum images containing a relatively low signal to noise ratio. Next, this technique is used to investigate the spatial distribution of Fe dopants in Fe:Ceria nanoparticles in 3D. It is shown that the presence of the Fe 2+ dopants is correlated with a reduction of the Ce atoms from Ce 4+ towards Ce 3+ . In addition, it is demonstrated that most of the Fe dopants are located near the voids inside the nanoparticle., (Copyright © 2016. Published by Elsevier B.V.)