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Tekari A.,University of Bern | Tekari A.,Institute for Surgical Technology and Biomechanics | Luginbuehl R.,RMS Foundation | Hofstetter W.,University of Bern | Egli R.J.,University of Bern
IEEE Transactions on Nanobioscience

Articular cartilage exists within synovial joints to adsorb and distribute mechanical loads to the subchondral bone. Mechanical loading is one aspect of a wide range of microenvironmental stressors that contribute to the maintenance of articular cartilage. The aim of the current study was to characterize bovine osteochondral tissues and to assess their suitability to serve as a model for investigating the effects of mechanical loading on cartilage tissue in vitro using a custom-made reactor system. Osteochondral tissues were harvested from bovine knee joints and cultured up to 24 days in loaded and unloaded conditions. Notably, we found a considerable zone-specific heterogeneity between cartilage explants harvested from the same joint as evidenced by histology and gene expression levels. Results using the reactor system revealed that differences observed after mechanical loading varied within the range of the heterogeneity observed amongst the different cartilage explants. Thus, it may be difficult to obtain reliable and reproducible data in mechanical loading experiments from these tissues in vitro, especially in cases where small variations between the experimental groups are expected. This will likely lead to the reporting of false positives or negatives in studies investigating the effect of mechanical load on the function of cartilage tissue. © 2015 IEEE. Source

Bardyn T.,Institute for Surgical Technology and Biomechanics | Gedet P.,Institute for Surgical Technology and Biomechanics | Hallermann W.,Inselspital | Buchler P.,Institute for Surgical Technology and Biomechanics
Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology and Endodontology

Objectives: Despite its importance, implant removal torque can be assessed at present only after implantation. This paper presents a new technique to help clinicians preoperatively evaluate implant stability. Study design: Planning software has been combined with an in-house finite element solver. Once the clinician has chosen the implant position on the planner, a finite element analysis automatically calculates the primary stability. The process was designed to be as simple and fast as possible for clinical use. This paper describes application of the method to the prediction of removal torque. A preliminary validation has been performed in both polyurethane foam and sheep bone. Results: The predicted torque is quantitatively equivalent to experimental values with correlation coefficients of >0.7 in both materials. Conclusions: This preliminary study is a first step toward the introduction of finite element models in computer-assisted surgery. The fact that the process is fast and automatic makes it suitable for a clinical use. © 2010 Mosby, Inc. All rights reserved. Source

Kozic N.,Institute for Surgical Technology and Biomechanics | Weber S.,Institute for Surgical Technology and Biomechanics | Buchler P.,Institute for Surgical Technology and Biomechanics | Lutz C.,Stryker Trauma GmbH | And 3 more authors.
Medical Image Analysis

Statistical shape analysis techniques have shown to be efficient tools to build population specific models of anatomical variability. Their use is commonplace as prior models for segmentation, in which case the instance from the shape model that best fits the image data is sought. In certain cases, however, it is not just the most likely instance that must be searched, but rather the whole set of shape instances that meet certain criterion. In this paper we develop a method for the assessment of specific anatomical/morphological criteria across the shape variability found in a population. The method is based on a level set segmentation approach, and used on the parametric space of the statistical shape model of the target population, solved via a multi-level narrow-band approach for computational efficiency. Based on this technique, we develop a framework for evidence-based orthopaedic implant design. To date, implants are commonly designed and validated by evaluating implant bone fitting on a limited set of cadaver bones, which not necessarily span the whole variability in the population. Based on our framework, we can virtually fit a proposed implant design to samples drawn from the statistical model, and assess which range of the population is suitable for the implant. The method highlights which patterns of bone variability are more important for implant fitting, allowing and easing implant design improvements, as to fit a maximum of the target population. Results are presented for the optimisation of implant design of proximal human tibia, used for internal fracture fixation. © 2010 Elsevier B.V. Source

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