In our correlative characterisation studies of biodegradable and permanent metal bone implants, we have performed both synchrotron-radiation microtomography (SR-μCT) and histology on the same samples. Histological staining is still the gold standard for tissue visualisation yet requires multiple time-consuming sample preparation steps (fixing, embedding, sectioning and staining) before imaging is performed on individual slices, in contrast to the non-invasive and 3D nature of tomography. In the process of correlating the corresponding data sets, we are able to combine advantages of both modalities by using machine learning methods to generate artificially stained 3D virtual histology datasets from SR-μCT datasets. For this we have developed an automated registration tool to find and fit the correct virtual tomographic plane to each histology slice. Preliminary results are promising after training a modified cycle generative adversarial network on our data, with two different histological stainings.
The combined application of x-ray diffraction contrast tomography and micro-computed tomography provides valuable and comprehensive insights, allowing for an in-depth understanding of phase composition and microstructure of materials. In this paper, we outline the current state of this methodology at the EH4 station of the P07 beamline, operated by Hereon, and present two practical applications within the field of metallic biomaterials for the development of new implant materials, inspiring further research and innovation in this area. Using magnesium alloys as implant materials offers several advantages, because they hold potential for temporary implants that naturally degrade within the body. These implants gradually dissolve over time, thereby promoting bone healing. Research in this area not only requires an understanding of the structural and phase changes in magnesium implants but also necessitates an investigation of the surrounding bone tissue structure. Although magnesium implant materials and bone tissue belong to different material classes, the combination of x-ray diffraction contrast tomography and micro-computed tomography enables detailed analysis of their microstructure and phase composition. As a result, we can extract information on porosity, phase composition, and crystal parameters around hybrid magnesium-titanium implants and obtain detailed information on the ultrastructure of bone tissue in a non-destructive manner.
This study explores recent developments in quantitative phase-contrast microtomography using Talbot Array Illuminators (TAI) combined with Unified Modulated Pattern Analysis (UMPA). We first compare the performance of the TAI-based method for phase-retrieval with propagation-based imaging (PBI) for analyzing a Mg-10Gd bone implant sample that violates the single-material assumption. Our results demonstrate that the TAI method yields a significantly higher contrast-to-noise ratio (CNR) compared to PBI (101.68 vs. 54.37, an 87% improvement) while maintaining comparable edge sharpness. The TAI method also visualizes a substructure of the degradation layer, which appears comparatively blurred in the PBI images. Additionally, we introduce a hanging-rotation-axis approach for imaging paraffin-embedded samples in an ethanol bath, aiming to reduce edge enhancement artifacts caused by large electron density differences. Preliminary results indicate that the TAI-based images of a paraffin-embedded lymph node show improved uniformity in background intensity, though some additional low-frequency noise is observed. All experiments were conducted at the High Energy Materials Beamline (HEMS), PETRA III, DESY, operated by Hereon. Our findings highlight the potential of TAI-based phase-contrast imaging for complex, multi-material samples and suggest avenues for further optimization of the technique.
The Helmholtz-Zentrum Hereon is operating imaging beamlines for X-ray tomography (P05 IBL, P07 HEMS) for academic and industrial users at the synchrotron-radiation source PETRA III at DESY in Hamburg, Germany. The high flux density and coherence of synchrotron radiation enable high-resolution in situ/operando/in vivo tomography experiments and phase-contrast imaging techniques, respectively. Large amounts of 3D and 4D data are collected that are difficult to process and analyze. Recently, we have explored machine learning approaches for the reconstruction, processing and analysis of synchrotron-radiation tomography data. Here, we report on the application of supervised learning for multimodal data analysis to generate a virtual 3D histology, digital volume correlation of 4D in situ tomography data, and instance segmentation. Furthermore, we present findings related to unsupervised learning in the context of semantic segmentation.
The Helmholtz-Zentrum Hereon is operating imaging beamlines for X-ray tomography (P05 IBL, P07 HEMS) for academic and industrial users at the synchrotron radiation source PETRA III at DESY in Hamburg, Germany. The high X-ray flux density and coherence of synchrotron radiation enables high-resolution in situ/operando/vivo tomography experiments and provides phase contrast, respectively. Large amounts of 3D/4D data are collected that are difficult to process and analyze. Here, we report on the application of machine learning for image segmentation including a guided interactive framework, multimodal data analysis (virtual histology), image enhancement (denoising), and self-supervised learning for phase retrieval.
KEYWORDS: Tomography, Image segmentation, Monte Carlo methods, Performance modeling, Bone, Process modeling, Data acquisition, Analytical research, Web services, Synchrotron radiation
The Helmholtz-Zentrum Hereon is operating several tomography end stations at the beamlines P05 and P07 of the synchrotron radiation facility PETRA III at DESY in Hamburg, Germany. Attenuation and phase contrast imaging techniques are provided as well as sample environments for in situ/operando/vivo experiments for applications in biology, medicine, materials science, etc. Very large and diverse data sets with varying spatiotemporal resolution, noise levels and artifacts are acquired which are challenging to process and analyze. Here we report on an active learning approach for the semantic segmentation of tomography data using a guided and interactive framework, and evaluate different acquisition functions for the selection of images to be annotated in the iterative process.
Hard X-ray nanotomography is a commonly used tool in many research areas such as material science, biology and medicine. The nanotomography station at the P05 imaging beamline at PETRA III at DESY is operated by the Helmholtz-Zentrum Hereon and optimized for full-field X-ray nanoimaging techniques. It offers spatial resolutions down to below 50 nm, as well as a high temporal resolution. The technical design allows for a high flexibility and is optimized for in situ experiments. The two major full-field techniques offered to the user community are transmission X-ray microscopy with optionally Zernike phase contrast and near-field holography. Here, the different full-field nanoimaging techniques, as well as the latest technical developments are presented.
Magnesium-based alloys are suitable materials for biodegradable bone implants due to their high biocompatibility and mechanical properties similar to bone. Diffraction tomography (DCT) is an imaging technique based on acquiring positionresolved diffraction patterns at multiple angular orientations. DCT is able to provide information about the crystal structure of hydroxyapatite (HAp) in bone, which is necessary for understanding the influence of magnesium degradation on the bone tissue. This article presents a study of a sheep bone explant containing a biodegradable magnesium-zinc-calcium (BRI.Mag, Mg-Zn-Ca) alloy implant. The DCT data were reconstructed and analyzed using the MATLAB computing environment and ASTRA Toolbox. Thus, the DCT images with 200 μm pixel size were reconstructed and, therefore, a DCT study workflow was developed. That allowed to retrieve the crystal lattice parameters of HAp such as full width at half maxima (FWHM) and reflection position around the implant surface of a selected (002) diffraction peak. Based on these parameters, the corresponding d-spacing and crystal size of HAp were determined. It was shown that the degradation of the implant does not notably affect the lattice spacing of the bone within a distance of 3 mm from the implant surface. At the same time, the crystal size decreases closer to the implant (1.5 mm). The developed workflow for the reconstruction of a volume-resolved diffraction experiment of a bone explant containing metal implants is a convenient approach to investigate the crystal structure of the sample on exact locations in a non-invasive way. The workflow will be further applied for a broader range of samples containing Mg-based biodegradable implants.
A load frame for in situ mechanical testing is developed for the microtomography end stations at the imaging beamline P05 and the high-energy material science beamline P07 of PETRA III at DESY, both operated by the Helmholtz- Zentrum Geesthacht. The load frame is fully integrated into the beamline control system and can be controlled via a feedback loop. All relevant parameters (load, displacement, temperature, etc.) are continuously logged. It can be operated in compression or tensile mode applying forces of up to 1 kN and is compatible with all contrast modalities available at IBL and HEMS i.e. conventional attenuation contrast, propagation based phase contrast and differential phase contrast using a grating interferometer. The modularity and flexibility of the load frame allows conducting a wide range of experiments. E.g. compression tests to understand the failure mechanisms in biodegradable implants in rat bone or to investigate the mechanics and kinematics of the tessellated cartilage skeleton of sharks and rays, or tensile tests to illuminate the structure-property relationship in poplar tension wood or to visualize the 3D deformation of the tendonbone insertion. We present recent results from the experiments described including machine-learning driven volume segmentation and digital volume correlation of load tomography sequences.
Permanent implants made of titanium or its alloys are the gold standard in many orthopedic and traumatological
applications due to their good biocompatibility and mechanical properties. However, a second surgical intervention is
required for this kind of implants as they have to be removed in the case of children that are still growing or on patient’s
demand. Therefore, magnesium-based implants are considered for medical applications as they are degraded under
physiological conditions. The major challenge is tailoring the degradation in a manner that is suitable for a biological
environment and such that stabilization of the bone is provided for a controlled period. In order to understand failure
mechanisms of magnesium-based implants in orthopedic applications and, further, to better understand the
osseointegration, screw implants in bone are studied under mechanical load by means of a push-out device installed at
the imaging beamline P05 of PETRA III at DESY. Conventional absorption contrast microtomography and phasecontrast
techniques are applied in order to monitor the bone-to-implant interface under increasing load conditions. In this
proof-of-concept study, first results from an in situ push-out experiment are presented.
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