Projekt 02FMTHH21

Predicting the influence of the nano-compositional bone architecture via finite element modeling


Osteoporosis, the most prevalent bone disorder in the world, is a significant risk factor for fragility fracture in elderly individuals. The bone fracture can result in chronic pain, decreased overall health status, reduced mobility and sometimes loss of independence, and finally has high clinical and economic burden. Human bone is a complex composite material with a hierarchical structure at different length scales, resulting in a unique mix of strength and toughness characterized by its constituents: mineral, collagen, and non-collagenous elements. Many of bone’s strength and toughness mechanisms are tied to the formation and repair of microcracks. High energy loading causes many microcracks in the bone structure, which disperse energy and prevent the formation of a major bone fracture. It is known that the crack propagation behavior of aged or diseased bone differs from that of healthy bone, resulting in a considerable drop in bone fracture toughness. To prevent the cracks from accumulating, bone’s ability to repair damaged tissue (e.g., micro-cracks), known as remodeling, comes into action, resorbing damaged bone and replacing it with new bone. A new osteon is formed as a result of the remodeling process, as well as a thin interface between the new osteons and the surrounding matrix. This interface layer, known as the cement line, is typically 2-5m thick and serves as a preferred site for micro-crack deflection. The remodeling process, which is a fundamental process in bone metabolism, is directed by mechanosensitive osteocytes embedded in the bone matrix. Their voids, known as osteocyte lacunae, indicate a gap in the bone structure. The dimensions and spatial layout of the lacuna differ considerably depending on age and disease. Their appearance in terms of size, form, and distribution can have a direct impact on bone strength and fracture risk, as well as lead to lower energy dissipation capacities, making the bone more fragile. As a result, the osteocyte lacuna arrangement, as well as the cement lines, play an important role in the mechanical properties of cortical bone, including fracture toughness and bone integrity. Changes in cortical bone microstructure may be linked to impaired fracture propagation behavior, resulting in altered bone toughness qualities. However, it is unclear how (age- or disease-related) changes in lacunar and osteonal architecture serve as either potential stress concentrators causing crack initiations or barriers slowing the propagation of micro-cracks. Today, new imaging methods like micro-computed tomography (1µm image resolution) and quantitative backscattered imaging allow for the quantification of bone structures and composition, allowing for the computational simulation of crack mechanisms. Notably, due to the challenges in experimental work, computational analysis such as the Finite Element Method (FEM) are powerful tools for studying bone fracture mechanisms and can be efficiently used to model competing toughening mechanisms and study microstructural effects on bone fracture properties.


The main goal of the proposed work is to develop a computational approach to model the crack initiation and propagation in cortical bone respecting cement line and osteon structure as well as osteocyte density and shape. Therefore, the disease- and age-related changes in the structural characteristics of the bone matrix including the osteocyte lacuna distribution and Haversian canal size and shape will be determined using micro computed tomography. Then, the analysis of the micro-crack initiation and propagation in the bone microstructure in presence of osteocyte lacunae and Haversian canals will be performed to assess the fracture toughness of the cortical bone. It is worth noting that during the analysis, the image segmentation due to precisely diagnosing the borders of cement lines around the osteon will be optimized to study the role of micro-crack initiation, propagation and accumulation mechanism in cortical bone.



  • S. Latus, J. Sprenger, M. Neidhardt, J. Schadler, A. Ron, A. Fitzek, M. Schlüter, P. Breitfeld, A. Heinemann, K. Püschel, A. Schlaefer (2021). Rupture detection during needle insertion using complex OCT data and CNNs. IEEE Transactions on Biomedical Engineering. 68 (10), 3059-3067.
  • M. Neidhardt, S. Gerlach, M.-H. Laves, S. Latus, C. Stapper, M. Gromniak, A. Schlaefer (2021). Collaborative robot assisted smart needle placement. Current Directions in Biomedical Engineering. 7. (2), 472-475
  • S. Latus, P. Breitfeld, M. Neidhardt, W. Reip, C. Zöllner, A. Schlaefer (2020). Boundary prediction during epidural punctures based on OCT relative motion analysis. EUR J ANAESTH. 2020 (Volume 37 | e-Supplement 58 | June 2020)
  • L. Bargsten, A. Schlaefer (2020). SpeckleGAN: a generative adversarial network with an adaptive speckle layer to augment limited training data for ultrasound image processing. International Journal of Computer Assisted Radiology and Surgery. 15 (9), 1427-1436.