Comparing Image Quality in Phase Contrast subμ\mu X-Ray Tomography -- A Round-Robin Study

How to evaluate and compare image quality from different sub-micrometer (sub$\mu$) CT scans? A simple test phantom made of polymer microbeads is used for recording projection images as well as 13 CT scans in a number of commercial and non-commercial scanners. From the resulting CT images, signal and noise power spectra are modeled for estimating volume signal-to-noise ratios (3D SNR spectra). Using the same CT images, a time- and shape-independent transfer function (MTF) is computed for each scan, including phase contrast effects and image blur ($\mathrm{MTF_{blur}}$). The SNR spectra and MTF of the CT scans are compared to 2D SNR spectra of the projection images. In contrary to 2D SNR, volume SNR can be normalized with respect to the object's power spectrum, yielding detection effectiveness (DE) a new measure which reveals how technical differences as well as operator-choices strongly influence scan quality for a given measurement time. Using DE, both source-based and detector-based sub$\mu$ CT scanners can be studied and their scan quality can be compared. Future application of this work requires a particular scan acquisition scheme which will allow for measuring 3D signal-to-noise ratios, making the model fit for 3D noise power spectra obsolete

Comparison of 2D and 3D Plasma Electrolytic Oxidation (PEO)-Based Coating Porosity Data Obtained by X-ray Tomography Rendering and a Classical Metallographic Approach

In this work, the porosity of plasma electrolytic oxidation (PEO)-based coatings on Al- and Mg-based substrates was studied by two imaging techniques-namely, SEM and computer microtomography. Two approaches for porosity determination were chosen; relatively simple and fast SEM surface and cross-sectional imaging was compared with X-ray micro computed tomography (microCT) rendering. Differences between 2D and 3D porosity were demonstrated and explained. A more compact PEO coating was found on the Al substrate, with a lower porosity compared to Mg substrates under the same processing parameters. Furthermore, huge pore clusters were detected with microCT. Overall, 2D surface porosity calculations did not show sufficient accuracy for them to become the recommended method for the exact evaluation of the porosity of PEO coatings; microCT is a more appropriate method for porosity evaluation compared to SEM imaging. Moreover, the advantage of 3D microCT images clearly lies in the detection of closed and open porosity, which are important for coating properties

A comparison of deep learning segmentation models for synchrotron radiation based tomograms of biodegradable bone implants

Introduction: Synchrotron radiation micro-computed tomography (SRμCT) has been used as a non-invasive technique to examine the microstructure and tissue integration of biodegradable bone implants. To be able to characterize parameters regarding the disintegration and osseointegration of such materials quantitatively, the three-dimensional (3D) image data provided by SRμCT needs to be processed by means of semantic segmentation. However, accurate image segmentation is challenging using traditional automated techniques. This study investigates the effectiveness of deep learning approaches for semantic segmentation of SRμCT volumes of Mg-based implants in sheep bone ex vivo.Methodology: For this purpose different convolutional neural networks (CNNs), including U-Net, HR-Net, U²-Net, from the TomoSeg framework, the Scaled U-Net framework, and 2D/3D U-Net from the nnU-Net framework were trained and validated. The image data used in this work was part of a previous study where biodegradable screws were surgically implanted in sheep tibiae and imaged using SRμCT after different healing periods. The comparative analysis of CNN models considers their performance in semantic segmentation and subsequent calculation of degradation and osseointegration parameters. The models’ performance is evaluated using the intersection over union (IoU) metric, and their generalization ability is tested on unseen datasets.Results and discussion: This work shows that the 2D nnU-Net achieves better generalization performance, with the degradation layer being the most challenging label to segment for all models

Overcoming water diffusion limitations in hydrogels via microtubular graphene networks for soft actuators

Hydrogel-based soft actuators can operate in sensitive environments, bridging the gap of rigid machines interacting with soft matter. However, while stimuli-responsive hydrogels can undergo extreme reversible volume changes of up to ~90%, water transport in hydrogel actuators is in general limited by their poroelastic behavior. For poly(N-isopropylacrylamide) (PNIPAM) the actuation performance is even further compromised by the formation of a dense skin layer. Here we show, that incorporating a bioinspired microtube graphene network into a PNIPAM matrix with a total porosity of only 5.4 % dramatically enhances actuation dynamics by up to ~400 % and actuation stress by ~4000 % without sacrificing the mechanical stability, overcoming the water transport limitations. The graphene network provides both untethered light-controlled and electrically-powered actuation. We anticipate that the concept provides a versatile platform for enhancing the functionality of soft matter by combining responsive and two-dimensional materials, paving the way towards designing soft intelligent matter.Comment: Shared First-authorship: Margarethe Hauck and Lena Marie Saur

Microengineered Hollow Graphene Tube Systems Generate Conductive Hydrogels with Extremely Low Filler Concentration

The fabrication of electrically conductive hydrogels is challenging as the introduction of an electrically conductive filler often changes mechanical hydrogel matrix properties. Here, we present an approach for the preparation of hydrogel composites with outstanding electrical conductivity at extremely low filler loadings (0.34 S m-1, 0.16 vol %). Exfoliated graphene and polyacrylamide are microengineered to 3D composites such that conductive graphene pathways pervade the hydrogel matrix similar to an artificial nervous system. This makes it possible to combine both the exceptional conductivity of exfoliated graphene and the adaptable mechanical properties of polyacrylamide. The demonstrated approach is highly versatile regarding porosity, filler material, as well as hydrogel system. The important difference to other approaches is that we keep the original properties of the matrix, while ensuring conductivity through graphene-coated microchannels. This novel approach of generating conductive hydrogels is very promising, with particular applications in the fields of bioelectronics and biohybrid robotics

Reply to: Revisiting life history and morphological proxies for early mammaliaform metabolic rates

Non peer reviewe

Reptile-like physiology in Early Jurassic stem-mammals

Despite considerable advances in knowledge of the anatomy, ecology and evolution of early mammals, far less is known about their physiology. Evidence is contradictory concerning the timing and fossil groups in which mammalian endothermy arose. To determine the state of metabolic evolution in two of the earliest stem-mammals, the Early Jurassic Morganucodon and Kuehneotherium, we use separate proxies for basal and maximum metabolic rate. Here we report, using synchrotron X-ray tomographic imaging of incremental tooth cementum, that they had maximum lifespans considerably longer than comparably sized living mammals, but similar to those of reptiles, and so they likely had reptilian-level basal metabolic rates. Measurements of femoral nutrient foramina show Morganucodon had blood flow rates intermediate between living mammals and reptiles, suggesting maximum metabolic rates increased evolutionarily before basal metabolic rates. Stem mammals lacked the elevated endothermic metabolism of living mammals, highlighting the mosaic nature of mammalian physiological evolution. Modern mammals are endothermic, but it has not been clear when this type of metabolism evolved. Here, Newham et al. analyse tooth and bone structure in Early Jurassic stem-mammal fossils to estimate lifespan and blood flow rates, which inform about basal and maximum metabolic rates, respectively, and show these stem-mammals had metabolic rates closer to modern ectothermic reptiles than to endothermic mammals.Peer reviewe

An image-based computation of microvascular dysfunction

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Dataset for an image-based computation of microvascular dysfunction

Dataset supports: Zeller-Plumhoff, B. (2017). An image-based computation of microvascular dysfunction. Doctor of Philosophy, University of Southampton. Funded by an EPSRC doctoral training grant.</span