Magnesium (Mg)-based implants are highly attractive for the orthopedic field and may replace titanium (Ti) as support for fracture healing. To determine the implant-bone-interaction in different bony regions, we implanted Mg-based alloy ZX00 (Mg < 0.5 Zn < 0.5 Ca, in wt%) and Ti-screws into the distal epiphysis and distal metaphysis of sheep tibiae. The implant degradation and osseointegration were assessed in vivo and ex vivo after 4, 6 and 12 weeks, using a combination of clinical computed tomography (cCT), medium-resolution micro CT (µCT) and high-resolution synchrotron radiation µCT (SRµCT). Implant volume loss, gas formation, and bone growth were evaluated for both implantation sites and each bone region independently. Additionally, histological analysis of bone growth was performed on embedded hard-tissue samples. We demonstrate that in all cases, the degradation rate of ZX00-implants ranges between 0.23-0.75 mm/year. The highest degradation rates were found in the epiphysis. Bone-to-implant-contact varied between the time points and bone types for both materials. Mostly, bone-volume-to-total-volume was higher around Ti-implants. However, we found an increased cortical thickness around the ZX00-screws when compared to the Ti-screws. Our results showed the suitability of ZX00-screws for implantation into the distal meta- and epiphysis.

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Magnesium–silver alloys are of high interest for the use as temporary bone implants due to their antibacterial properties in addition to biocompatibility and biodegradability. Thin wires in particular can be used for scaffolding, but the determination of their degradation rate and homogeneity using traditional methods is difficult. Therefore, we have employed 3D imaging using X-ray near-field holotomography with sub-micrometer resolution to study the degradation of thin (250 μm diameter) Mg-2Ag and Mg-6Ag wires. The wires were studied in two states, recrystallized and solution annealed to assess the influence of Ag content and precipitates on the degradation. Imaging was employed after degradation in Dulbecco’s modified Eagle’s medium and 10% fetal bovine serum after 1 to 7 days. At 3 days of immersion the degradation rates of both alloys in both states were similar, but at 7 days higher silver content and solution annealing lead to decreased degradation rates. The opposite was observed for the pitting factor. Overall, the standard deviation of the determined parameters was high, owing to the relatively small field of view during imaging and high degradation inhomogeneity of the samples. Nevertheless, Mg-6Ag in the solution annealed state emerges as a potential material for thin wire manufacturing for implants.

Highly accurate segmentation of large 3D volumes is a demanding task. Challenging applications like the segmentation of synchrotron radiation microtomograms (SRμCT) at high-resolution, which suffer from low contrast, high spatial variability and measurement artifacts, readily exceed the capacities of conventional segmentation methods, including the manual segmentation by human experts. The quantitative characterization of the osseointegration and spatio-temporal biodegradation process of bone implants requires reliable, and very precise segmentation. We investigated the scaling of 2D U-net for high resolution grayscale volumes by three crucial model hyper-parameters (i.e., the model width, depth, and input size). To leverage the 3D information of high-resolution SRμCT, common three axes prediction fusing is extended, investigating the effect of adding more than three axes prediction. In a systematic evaluation we compare the performance of scaling the U-net by intersection over union (IoU) and quantitative measurements of osseointegration and degradation parameters. Overall, we observe that a compound scaling of the U-net and multi-axes prediction fusing with soft voting yields the highest IoU for the class “degradation layer”. Finally, the quantitative analysis showed that the parameters calculated with model segmentation deviated less from the high quality results than those obtained by a semi-automatic segmentation method.

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